As a typical non-Gaussian vector variable, a neutral vector variable contains nonnegative elements only, and its l1 -norm equals one. In addition, its neutral properties make it significantly different from the commonly studied vector variables (e.g., the Gaussian vector variables). Due to the aforementioned properties, the conventionally applied linear transformation approaches [e.g., principal component analysis (PCA) and independent component analysis (ICA)] are not suitable for neutral vector variables, as PCA cannot transform a neutral vector variable, which is highly negatively correlated, into a set of mutually independent scalar variables and ICA cannot preserve the bounded property after transformation. In recent work, we proposed an efficient nonlinear transformation approach, i.e., the parallel nonlinear transformation (PNT), for decorrelating neutral vector variables. In this article, we extensively compare PNT with PCA and ICA through both theoretical analysis and experimental evaluations. The results of our investigations demonstrate the superiority of PNT for decorrelating the neutral vector variables. © 2012 IEEE.

智能化的普及对图像编辑提出了新需求,海报作为一种以图像形式传递信息的方式,在日常生活和工作管理中起着重要的作用。海报的制作需要多元素图像进行合成,目前缺少一种交互式的、一键式的图像合成系统,因此,结合当前流行的图像处理技术

随着智能电网的普及和大数据技术的发展，利用用电数据分析用户的用电行为越来越受到关注，现存的能源分解方法无法满足实际应用中对分辨率和分解准确率的高要求，以及聚类分析方法过于粗糙没有充分挖掘每类电器的用电特点。提出了基于能源分解的用户用电行为分析方法。在判别式稀疏编码算法模型的基础上，针对L0正则项不易求解、L1正则项稀疏约束效果不理想的问题，提出用L1/2正则项稀疏约束进行能源分解，并且把用户之间的同质性作为正则项加入基础模型来修正模型的性能。基于能源分解的结果，使用用户单类电器的用电特征代替总用电特征精细化分析用户的用电行为，并改进传统的K-Mean聚类算法进行实验验证。实验结果表明：所提出的基于L1/2正则项稀疏约束和同质性约束的能源分解方法相比于传统判别式稀疏编码算法，能够有效提升能源分解的准确率。同时，基于能源分解的用户用电行为聚类分析效果也有明显提升。

With the popularization of smart grids and the development of big data technology, more and more attention has been paid to the analysis of users' electricity consumption behavior through electricity consumption data. The existing energy decomposition methods cannot meet the high requirements for resolution and decomposition accuracy in practical applications, and the cluster analysis method is too rough and does not fully show the electricity consumption characteristics of each type of electrical appliances. In view of this, this paper proposes an analysis method of users' electricity consumption behavior based on energy decomposition. Based on the discriminative sparse coding algorithm model, firstly, to solve the problem that the regular term of L0 is not easy to solve and the effect of the sparse constraint of L1 regular term is not ideal, we propose to use the sparse constraint of L1/2 regular term to perform energy decomposition, and add the homogeneity between users as a regular term to the basic model to modify the performance of the model. Secondly, based on the results of energy decomposition, we use the electricity consumption characteristics of a user's single-type electrical appliances instead of the total electricity consumption characteristics to refine the analysis of user's electricity consumption behavior, and improve the traditional K-Mean clustering algorithm for experimental verification. The experimental results show that the energy decomposition method based on the sparse constraint of L1/2 regular term and the constraint of homogeneity can effectively improve the accuracy of energy decomposition compared with the traditional discriminative sparse coding method. At the same time, the result of cluster analysis of users' electricity consumption behavior based on energy decomposition is also significantly improved. © 2022, Editorial Board of JBUAA. All right reserved.

The popularization of intelligence puts forward new requirements for image editing. As a way of transmitting information in the form of images, posters play an important role in daily life and work management. But the production of posters requires multi-element image synthesis. However, there is a lack of an interactive and one click image synthesis system. Combined with the current popular image processing technology, a poster automatic synthesis system is designed and implemented. We propose keyword-based image retrieval scheme, constructs a dual filtering scheme based on text and content, and provides users with accurate and fast image retrieval methods. By counting the composition rules of a large number of carefully designed poster pictures and introducing the composition rules of aesthetic common sense, we propose a portrait layout recommendation scheme based on two-way rules, which assists users in portrait layout design under the combined effect of two-way rules. The experimental results prove that the scheme designed in this paper can run stably and efficiently, users can realize image synthesis through simple interactive operations, and the final image synthesis effect is real and effective. © 2022, Editorial Board of JBUAA. All right reserved.