A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

沭阳县新沂河抗洪巡堤查险"六全"工作法

2019年和2020年连续两年,由于受台风影响及上游洪水袭击,新沂河大流量、高水位行洪,本文介绍了沭阳县全力以赴,立足于防,着力于抢,及时启动防汛应急机制,严密巡堤查险,迅速组织抢险,取得了防洪抗洪的全面胜利,有力保障了新沂河两岸200万人民生命财产安全和全县经济社会平稳发展,实现了抗大洪、无大灾、零伤亡的预期目标.     

非线性干涉滤光片开关特性及逻辑功能

应用干涉滤光片作为一种光学双稳器件,演示了光学逻辑开关功能。     

WLAN网管系统的现状和发展

2001年以来，全球WLAN（无线局域网）市场应用的瓶颈被打破，中国也迎来了WLAN商业化运作和服务的春天，WLAN为我们带来了一种全新的、灵活的工作方式，同时也带来了新的挑战，本文将对WLAN网络管理的主要内容和具体指标进行阐述，并对各大厂商的WLAN网管产品进行简单介绍和比较。     

Stable social foraging swarms in a noisy environment

Bacteria, bees, and birds often work together in groups to find food. A group of robots can be designed to coordinate their activities to search for and collect objects. Networked cooperative uninhabited autonomous vehicles are being developed for commercial and military applications. Suppose that we refer to all such groups of entities as "social foraging swarms". In order for such multiagent systems to succeed it is often critical that they can both maintain cohesive behaviors and appropriately respond to environmental stimuli (e.g., by optimizing the acquisition of nutrients in foraging for food). In this paper, we characterize swarm cohesiveness as a stability property and use a Lyapunov approach to develop conditions under which local agent actions will lead to cohesive foraging even in the presence of "noise" characterized by uncertainty on sensing other agent's position and velocity, and in sensing nutrients that each agent is foraging for. The results quantify earlier claims that social foraging is in a certain sense superior to individual foraging when noise is present, and provide clear connections between local agent-agent interactions and emergent group behavior. Moreover, the simulations show that very complicated but orderly group behaviors, reminiscent of those seen in biology, emerge in the presence of noise.     

Density of Carier in YBCO Superconductors with (Ca,Th) Doping

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Cross lingual speech emotion recognition via triple attentive asymmetric convolutional neural network

The application of cross‐corpus for speech emotion recognition (SER) via domain adaptation methods have gain high acknowledgment for developing good robust emotion recognition systems using different corpora or datasets. However, the issue of cross‐lingual still remains a challenge in SER and needs more attention to resolve the scenario of applying different language types in both training and testing. In this paper, we propose a triple attentive asymmetric convolutional neural network to address the recognition of emotions for cross‐lingual and cross‐corpus speech in an unsupervised approach. The proposed method adopts the joint supervision of softmax loss and center loss to learn high power discriminative feature representations for target domain via the use of high quality pseudo‐labels. The proposed model uses three attentive convolutional neural networks asymmetrically, where two of the networks are used to artificially label unlabeled target samples as a result of their predictions from training on source labeled samples and the other network is used to obtain salient target discriminative features from the pseudo‐labeled target samples. We evaluate our proposed method on three different language types (i.e., English, German, and Italian) data sets. The experimental results indicate that, our proposed method achieves higher prediction accuracy over other state‐of‐the‐art methods.     

多媒体课件改变了教师的“主讲”地位

随着高等教育教学改革步伐和数字化教学的不断推进和发展,越来越多的教师热衷于多媒体教学课件的使用和研究,传统的教学模式被逐渐取代,教师教学的主导地位越来越不明显,数字教学的时代悄然而至。     

基于关联分析的变电站附属设备差异化运维

针对变电站附属设施缺陷分析手段欠缺、运维策略粗放、简单周期化的问题,研究基于关联分析的变电站附属设备差异化运维技术,基于现有的变电站附属设备管理系统搜集的缺陷历史数据,从变电站、附属设备类型、厂家、型号和投运年限、季节等维度对变电站附属设备进行关联分析,实现附属设备的状态评估,提出附属设备差异化运维方案,从而提升运维人员决策的预见性和全局性.     

PAN/FPU Composite Nanofiber Membrane with Superhydrophobic and Superoleophobic Surface as a Filter Element for High-Efficiency Protective Masks

Recently, because of the outbreak of COVID-19, the demand for various types of filter elements in protective materials has increased globally. Furthermore, new requirements for the filtration performance of PM2.5 liquid (oil) particles have been put forward. In this work, Superhydrophobic and superoleophobic composite nanofibers with excellent filtration capacity for oil and salt particles are developed through the modification of polyacrylonitrile (PAN) by fluoro-polyurethane (FPU) doping. The results show that the PAN/FPU composite nanofibers doped with 9 wt% FPU has a uniform fiber morphology with a diameter of 240 ± 30 nm. Compared to the pure PAN nanofibers, the water-based contact angle of PAN/FPU increases from 90 ± 5° to 151 ± 5°, and the oil-based contact angle increases from 58 ± 2° to 152 ± 3°. Importantly, at a high flow rate of 95 L min⁻¹, the filtration efficiency of the PAN/FPU nanofiber membrane for 0.3 µm oil particles increases from 92 ± 1% to 99.2 ± 0.1%. After cyclic loading, the filtration efficiency of 0.3 µm oil particles remains above 98%. Meanwhile, the filtration efficiency for 0.3 µm salt particles remains at 98.23 ± 0.1%. The PAN/FPU nanofiber membrane developed in this work is effective in applications and has good market prospects as a protective filtration material.