CONTOUR: an efficient algorithm for discovering discriminating subsequences

In recent years we have witnessed several applications of frequent sequence mining, such as feature selection for protein sequence classification and mining block correlations in storage systems. In typical applications such as clustering, it is not the complete set but only a subset of discriminating frequent subsequences which is of interest. One approach to discovering the subset of useful frequent subsequences is to apply any existing frequent sequence mining algorithm to find the complete set of frequent subsequences. Then, a subset of interesting subsequences can be further identified. Unfortunately, it is very time consuming to mine the complete set of frequent subsequences for large sequence databases. In this paper, we propose a new algorithm, CONTOUR, which efficiently mines a subset of high-quality subsequences directly in order to cluster the input sequences. We mainly focus on how to design some effective search space pruning methods to accelerate the mining process and discuss how to construct an accurate clustering algorithm based on the result of CONTOUR. We conducted an extensive performance study to evaluate the efficiency and scalability of CONTOUR, and the accuracy of the frequent subsequence-based clustering algorithm.     

基于模板法的网页英语试卷自动抽取技术的研究

为解决在线考试系统中建立海量数据库的问题,采用基于模板法的Web信息抽取方法,提取相似网页中的正文内容。并根据包含英文试卷的网页特点,制定正文抽取规则,最终可获得完整的英语试卷及其答案。实验结果表明,该方法具有较高的准确率和提取速度。     

Optimizing the Oxygen-Catalytic Performance of Zn–Mn–Co Spinel by Regulating the Bond Competition at Octahedral Sites

By using the more electro-negative Mn³⁺ ion to partially replace Co³⁺ at the octahedral site of spinel ZnCo₂O₄, i.e., forming ternary Zn–Mn–Co spinel oxide, the electrocatalytic oxygen reduction/evolution activity is found to be significantly increased. Considering the physical characterization and theoretical calculations, it demonstrated that the bond competition played a key role in regulating the cobalt valence state and the electrocatalytic activity. The partial replacement of octahedral-site-occupied Co³⁺ by Mn³⁺ can effectively modulate the adjacent Co–O bond and induce the Jahn–Teller effect, thus changing the originally stable crystal structure and optimizing the binding strength between the active center and reaction intermediates. Certainly, the Mn-substituted ZnMn_1.4Co_0.6O₄/NCNTs exhibit higher electrocatalytic oxygen reduction reaction (ORR) activity than that of ZnCo₂O₄/NCNTs and ZnMn₂O₄/NCNTs, supporting that the Co–O bond covalency determines the ORR activity of spinel ZnCo₂O₄. This study offers the competition between adjacent Co–O and Mn–O bonds via the B_Oh–O–B_Oh edge-sharing geometry. The ion substitution at octahedral sites by less electronegative cations can be a new and effective way to improve the electrocatalytic performance of cobalt-based spinel oxides.     

Neural network robust control of a 3-DOF hydraulic manipulator with asymptotic tracking

Multiaxial hydraulic manipulators are complicated systems with highly nonlinear dynamics and various modeling uncertainties, which hinders the development of high-performance controller. In this paper, a neural network feedforward with a robust integral of the sign of the error (RISE) feedback is proposed for high precise tracking control of hydraulic manipulator systems. The established nonlinear model takes three-axis dynamic coupling, hydraulic actuator dynamics, and nonlinear friction effects into consideration. A radial basis function neural network (RBFNN) is synthesized to approximate the uncertain system dynamics and external disturbance, which can greatly reduce the dependence on accurate system model. In addition, a continuous RISE feedback law is judiciously integrated to deal with the residual unknown dynamics. Since the major unknown dynamics can be estimated by the RBFNN and then compensated in the feedforward design, the high-gain feedback issue in RISE feedback control will be avoided. The proposed RISE-based neural network robust controller theoretically guarantees an excellent semi-global asymptotic stability. Comparative simulation is performed on a 3-DOF hydraulic manipulator, and the obtained results verify the effectiveness of the proposed controller.     

磁悬浮储能飞轮高速永磁电机设计及优化

高速磁悬浮储能飞轮是一种新型高比功率储能装置,其中高速大功率电机是能量转换与传输控制的核心。本文针对所研究的300kW磁悬浮飞轮储能系统的应用特性提出了一种轴式永磁同步电机方案,从电磁、结构和控制等方面进行综合分析与设计;在此基础上,为了适应高速稳定运行、高功率放电对高速电机提出的新要求,从转子的结构强度、电机损耗、运行区间的效率、转矩波动等方面出发对电机的设计参数进行优化分析。结果表明：优化后的电机结构强度高,能够满足储能飞轮在高转速、高功率连续充放电等工况下的使用要求,15000～30000r/min工作转速范围内电机效率达到90%以上。     

Social-path embedding-based transformer for graduation development prediction

As the education of students attracts more and more attention, the task of graduation development prediction has gradually become a hot topic in academia and industry. The task of graduation development prediction aims to predict the employment category of students in advance via academic achievement data, which can help administrators understand students’ learning status and set up a reasonable learning plan. However, existing research ignores the potential impact of social relationships on students’ graduation development choices. To fully explore social relationships among students, we propose a Social-path Embedding-based Transformer Neural Network (SPE-TNN) for the task of graduation development prediction in this paper. Specifically, SPE-TNN is divided into the Social-path selection layer, the Social-path embedding layer, the Transformer layer, and the Multi-layer projection layer. Firstly, the Social-path selection layer is designed to find social relationships that impact graduation development and embed them into the student’s performance features through the Social-path embedding layer. Secondly, the Transformer layer is adopted to balance the weights of the students’ features. Finally, the Multi-layer projection layer is used to achieve the student graduation development prediction. Experimental results on the real-world datasets show that SPE-TNN outperforms the existing popular approaches.

     

UV Photodetectors Based on BiOCl Nanosheet Arrays: The Effects of Morphologies and Electrode Configurations

A facile chemical bath method is adopted to grow bismuth oxychloride (BiOCl) nanosheet arrays on a piece of Cu foil (denoted as BiOCl‐Cu) and isolated BiOCl nanosheets are collected by ultrasonication. A self‐supporting BiOCl film is obtained by the removal of Cu foil. Photodetectors (PDs) based on these BiOCl materials are assembled and the effects of morphologies and electrode configurations on the photoelectric performance of these PDs are examined. The BiOCl nanosheet PD achieves high responsivities in the spectral range from 250 to 350 nm, while it presents quite a small photocurrent and slow response speed. The BiOCl film PD yields low photocurrents and near‐unity on–off ratios, demonstrating poor photoelectric performance. The photocurrent of the BiOCl‐Cu PD with both electrodes on the BiOCl film is much higher than those of these above‐mentioned PDs, and the response times are fast. Meanwhile, the BiOCl‐Cu PD with separate electrodes on the BiOCl film and Cu foil achieves even higher photocurrents and presents a self‐powering characteristic, depicting the improved photodetecting performances induced by the specific morphology and distinct electrode configuration. These results would promote the applications of BiOCl nanostructures in the photoelectric devices.     

Dynamic mix zone: location data sanitizing in assisted environments

Pervasive technology has been widely used in assistive environments and aware homes. The issue of how to preserve the privacy of patients being monitored has been attracting more public concerns. In assistive environments, location data of patients are collected through sensors for behavior patterns analysis, and they can also be shared among researchers for further research for early disease diagnosis. However, location information, even though de-identified, also introduces the risk of privacy leakage. A series of consecutive location samples can be considered as a trajectory of a single person, and this may leak private information if obtained by malicious users. This paper discusses this problem and proposes a location randomization algorithm to protect users’ location privacy. Two privacy metrics according to location privacy are defined and used to evaluate the proposed approach. A method using dynamic mix zones is proposed to confound trajectories of two or more persons.     

Diverse community: Demand differentiation in P2P live streaming

Peer-to-Peer (P2P) technology has become an attractive approach for enabling large-scale video streaming applications, but the factor of users’ subjective preferences is not paid enough attention in such networks. As users have various demand on video qualities, we can provide them with video streams at different resolutions without impairing their satisfaction. The adaptive streaming rate technique is a promising method. However, in providing adaptive streaming rate services, P2P live streaming design faces the following challenge: how to provide all users with uninterrupted video with their desired qualities in case that their demand dynamically changes? To shed more light on this problem, we first derive a model and formulate the problem as a resource demand vs supply problem. Then we present a framework to address the challenge via efficient bandwidth allocation and group cooperation. Through comprehensive simulations, we evaluate the effectiveness of the proposed framework, and conclude that it effectively helps existing solutions, such as Partial Participation Scheme (PPS), achieve better performance.