A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.     

Global robust output regulation of lower triangular systems with unknown control direction

This paper presents the solvability conditions for the global robust output regulation problem for lower triangular nonlinear systems assuming the control direction is unknown. The approach used is an integration of the robust stabilization technique and Nussbaum gain technique.     

Aperture-Adjustable and Repairable Metal-Based MOFs Catalysis Membrane for Water Purification

Precise adjustment of the pore size, damage repair, and efficient cleaning is all challenges for the wider application of inorganic membranes. This study reports a simple strategy of combining dry-wet spinning and electrosynthesis to fabricate stainless-steel metal–organic framework composite membranes characterized by customizable pore sizes, targeted reparability, and high catalytic activity for membrane cleaning. The membrane pore size can be precisely customized in the range of 14–212 nm at nanoscale, and damaged membranes can be repaired by targeted treatment in 120 s. In addition, advanced oxidation processes can be used to quickly clean the membrane and achieve 98% flux recovery. The synergistic actions of the membrane matrix and the selective layer increase the adsorption energy of active sites to oxidant, shorten the electron transfer cycle, and enhance the overall catalytic performance. This study can provide a new direction for the development of advanced membranes for water purification and high-efficiency membrane cleaning methods.     

Design of a 2-DOF-PID controller using an improved sine–cosine algorithm for load frequency control of a three-area system with nonlinearities

This paper proposes an improved sine–cosine algorithm (ISCA) based 2-DOF-PID controller for load frequency control. A three-area test system is built for study, while some physical constraints (nonlinearities) are considered for the investigation of a realistic power system. The proposed method is used as the parameter optimizer of the LFC controller in different scenarios. The 2-DOF-PID controllers are used because of their capability of fast disturbance rejection without significant increase of overshoot in set-point tracking. The 2-DOF-PID controllers’ efficacy is observed by examining the responses with the outcomes obtained with PID and FOPID controllers. The simulation results with the suggested scheme are correlated with some of the existing algorithms, such as SCA, SSA, ALO, and PSO in three different scenarios, i.e., a disturbance in two areas, in three areas, and in the presence of physical constraints. In addition, the study is extended to a four-area power system. Statistical analysis is performed using the Wilcoxon Sign Rank Test (WSRT) on 20 independent runs. This confirms the supremacy of the proposed method.     

基于区块链的物联网信息安全技术研究

物联网的发展在这几年获得的成果非常明显,在实现智能化管理与操作的同时,存在的隐私安全以及漏洞问题也暴露出来。物联网规模非常庞大,并且还具有分散的拓扑结构。传统的安全与隐私保护方式具有局限性,对于现阶段的物联网操作系统并没有太大的作用。因此,为了保障物联网信息的安全性,弥补传统防护技术上的缺陷,物联网信息防护技术的研究迫在眉睫。     

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.

     

Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network

Computational Economics - The study aims to analyze and forecast Internet financial risks based on the model based on deep learning and the Back Propagation Neural Network (BPNN). First, the theory...     

Design and analysis of turnover platform for welding robot北大核心CSCD

A turnover platform for welding robot was designed for the application of welding robot with lower accuracy requirement, which was of low cost and higher position accuracy. In this turnover platform, the pneumatic motor was used as the power output, and the indexing mechanism with high accuracy was the transmission system with high transmitting ratio based on worm and wheel. The position information was acquired by using the photoelectric encoder, and the turnover motion with high accuracy was realized through the closed-loop controller. Simulation results showed that the maximum speed of the welding platform approached 14 r/min, and the platform could meet the requirements of most welding products. Such a turnover platform can offer the application program of the welding robot with low cost for the middle and low level products, and reduce the cost of welding robot and improve welding productivity.     

设计的进化与价值

<正>一、前言设计是人们对于装备制造、工程建设、经营管理和服务创新的创意设想、计划和计算,是将信息、知识和技术转化、集成为整体解决方案并实现价值的发明创造,是一切有目标创新实践的先导、准备与关键环节。设计决定着创新创造的方向、目标、路径和价值。设计创新引领推动人类社会的文明和进步,设计的知识技术、价值理念、人才团队、方式环境也随着人类文明进化而发展。从旧石器时代起,我们的祖先已利用天然材料,朦胧地构想制作