KWTA networks and their applications

Winner-Take-All (WTA) or K-Winner-Take-All (KWTA) networks have been frequently used as the basic building blocks of complex neural networks. This paper introduces a new selection rule for network connections that implements stable KWTA networks. To widen the applications of WTA networks, a new class of WTA networks is proposed, and their efficient design methods are presented. We demonstrate the properties of the generalized class of WTA networks, through three application examples.     

TiO_2对ZrO_2－NiCr金属陶瓷烧结特性和相稳定性的影响

本文研究了ＴｉＯ２掺入对ＣａＯ稳定ＺｒＯ２（简称ＣａＳＺ－ＮｉＣｒ）金属陶瓷烧结特性的影响，测定了ＣａＳＺ－ＮｉＣｒ－ＴｉＯ２金属陶瓷烧结样品的密度和显微硬度，结果表明，ＴｉＯ２的掺入能提高ＣａＳＺ－ＮＩＣｒ金属陶瓷的烧结密度和显微硬度，对ＣａＳＺ－ＮｉＣｒ金属陶瓷有助烧结作用．金相观察表明，ＴｉＯ３的掺入改变了ＮｉＣｒ相在金属陶瓷中的形貌，说明了ＮｉＣｒ与ＣａＳＺ的浸润性有一定的改善．ＸＲＤ和ＥＰＭＡ分析结果显示，ＴｉＯ２偏聚在ＮｉＣｒ金属相中，不影响ＣａＳＺ的稳定性．     

太湖流域某净水厂嗅味物质变化特征研究

为了探讨净水厂中嗅味物质的浓度变化规律与各级处理单元之间的关系,以期为净水厂更加高效地去除水中的嗅味物质提供依据,采用固相微萃取—气质联用技术对太湖流域某净水厂中的2-甲基异莰醇(2-methylisobornel,2-MIB)、土臭素(Geosmin,GSM)、β-紫罗兰酮(β-Ionone,p-Ion)和β环柠檬醛(pCyclocitrial,p-Cyc)4种特征嗅味物质进行检测,并分析比较了净水厂各级处理单元对嗅味物质的去除效果.结果表明:β-Cyc的平均浓度最高,为5.83 μg/L,其次为β-Ion,平均浓度达到41.36 ng/L,2-MIB和GSM的平均浓度相对较低,分别为4.02 ng/L、l.13 ng/L;净水厂处理工艺对2-MIB、β-Cyc、β-Ion和GSM的去除率分别为99.93％、93.73％、64.09％和0;净水厂的处理工艺对嗅味物质具有一定的去除效果,嗅味物质浓度沿着水处理工艺流程呈下降趋势,嗅味物质的去除主要发生在深度处理阶段.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

Land‐use dynamic discovery based on heterogeneous mobility sources

Nowadays, cities are the most relevant type of human settlement and their population has been endlessly growing for decades. At the same time, we are witnessing an explosion of digital data that capture many different aspects and details of city life. This allows detecting human mobility patterns in urban areas with more detail than ever before. In this context, based on the fusion of mobility data from different and heterogeneous sources, such as public transport, transport‐network connectivity and Online Social Networks, this study puts forward a novel approach to uncover the actual land use of a city. Unlike previous solutions, our work avoids a time‐invariant approach and it considers the temporal factor based on the assumption that urban areas are not used by citizens all the time in the same manner. We have tested our solution in two different cities showing high accuracy rates.     

Parallel source separation system for heart and lung sounds

The Journal of Supercomputing - In this paper, we propose a parallel source separation system designed to extract heart and lung sounds from single-channel mixtures. The proposed system is based on...     

A comparison study of wavelet transforms for the visualization of differentially methylated regions in DNA samples

The Journal of Supercomputing - DNA methylation analysis has become an important topic in the study of human health. DNA methylation analysis requires not only a specific treatment of DNA samples...     

Trajectory tracking control for underactuated unmanned surface vehicle subject to uncertain dynamics and input saturation

Neural Computing and Applications - In this paper, one concerns with the problem of trajectory tracking control for an underactuated unmanned surface vehicle subject to uncertain dynamics and input...     

基于正切图像处理(TIP)模型的自适应图像增强算法

针对图像增强算法复杂度高、对噪声敏感等问题,在分析对数图像处理模型的基础上,提出了一种基于正切图像处理模型的自适应图像增强算法,该算法具有实现容易、运算速度快等特点,对噪声具有一定的抑制作用,可以增强和保留图像细节信息。既可以增强灰度图像,又可以增强彩色图像,实验和理论分析说明了该算法的优越性。