New results in 2D optimal control theory

The infinite horizon optimal control problem is solved for 2D systems described by the Fornasini-Marchesini model. An ₂-approach permits us to reduce the optimal control problem to a norm minimization one in Hilbert spaces. Both necessary and sufficient conditions for solvability and the structure of the solution are established. Moreover, a comparison with known results is presented, and the singularities of the 2D Riccati equation are examined in order to characterize suboptimal control laws that apply whenever the solvability conditions are not satisfied.     

On structure and stability of (Hg, Mo)-1212 superconductors based on SAED and EDS analysis

A superconductor with nominal composition Hg_0.8Mo_0.2Sr₂Y_0.5Ca_0.5Cu₆₊ withT _c up to 96 K has been isolated and carefully examined by electron diffraction and X-ray energy dispersive spectrum analyses. Its structure can be attributed to space groupP4/mmm and lattice constantsa=3.82 å andc=11.91 å. Inhomogeneous distribution of the cations was observed, which is considered to be the key to the broadening ofT _c in theR-T curve. The effect of humid atmosphere on the stability of the sample has also been investigated.     

基于FMCW雷达的呼吸模式分类技术研究

针对当前使用调频连续波雷达的呼吸模式分类算法准确度不高的问题,本文提出一种基于一维卷积神经网络（1DCNN）结合长短时记忆（LSTM）网络的多呼吸模式分类方法。方法共分为四步：第一步,对雷达提取的呼吸信号进行预处理;第二步,使用快速傅里叶变换（FFT）与连续小波变换（CWT）提取呼吸信号特征;第三步,根据呼吸特征对五种呼吸模式信号（正常呼吸、呼吸过速、呼吸过缓、呼吸深大、呼吸暂停）打标签制作数据集;第四步,使用数据集训练网络得到模型,并使用新数据测试模型。实验结果表明,此方法分类准确度要比现有使用CNN网络方法高5%左右。     

Halogenated Ti3C2 MXenes Prepared by Microwave Molten Salt for Hg0 Photo-Oxidation

Ti₃C₂ MXenes with different halogen modifications are prepared rapidly and efficiently by microwave molten salt method, and the MXene surface functional group modification is successfully achieved to address the problems of low purity, complex functional groups, and uncontrollable energy band structure of MXenes obtained by traditional liquid phase etching. Among them, the modification of the iodine (I) functional group onto the surface of Ti₃C₂ changes the energy band structure and band gap, resulting in easier photoexcitation and more photogenerated carriers. The increased Fermi energy is closer to the conduction band, the decreased surface work function weakens the electron confinement ability. The photogenerated carriers can migrate to the surface of the material more easily with extended lifetime, so the activity of the catalyst is improved. Further, for gaseous monomeric mercury (Hg⁰) photo-oxidative removal, Ti₃C₂-I₂ exhibits 85.5% efficiency of Hg⁰ photo-oxidative removal under visible light. Based on the experimental characterization and density functional theory calculations, a mechanism for the photo-oxidative removal of Hg° from Ti₃C₂-I₂ MXene is proposed, which provides a valuable strategy for studying Ti₃C₂ MXenes in the field of photocatalysis.     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

Association Rule Mining Frequent-Pattern-Based Intrusion Detection in Network

In the network security system, intrusion detection plays a significant role. The network security system detects the malicious actions in the network and also conforms the availability, integrity and confidentiality of data information resources. Intrusion identification system can easily detect the false positive alerts. If large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine attacks. Many research works have been done. The issues in the existing algorithms are more memory space and need more time to execute the transactions of records. This paper proposes a novel framework of network security Intrusion Detection System (IDS) using Modified Frequent Pattern (MFP-Tree) via K-means algorithm. The accuracy rate of Modified Frequent Pattern Tree (MFPT)-K means method in finding the various attacks are Normal 94.89%, for DoS based attack 98.34%, for User to Root (U2R) attacks got 96.73%, Remote to Local (R2L) got 95.89% and Probe attack got 92.67% and is optimal when it is compared with other existing algorithms of K-Means and APRIORI.     

RTDNet：面向高分辨率卫星影像的赤潮探测网络

目的 赤潮是一种常见的海洋生态灾害,严重威胁海洋生态系统安全。及时准确获取赤潮的发生和分布信息可以为赤潮的预警和防治提供有力支撑。然而,受混合像元和水环境要素影响,赤潮分布精细探测仍是挑战。针对赤潮边缘探测的难点,结合赤潮边缘高频特征学习与位置语义,提出了一种计算量小、精度高的网络模型RTD-Net （red tide detection network）。方法 针对赤潮边缘探测不准确的问题,设计了基于RIR （residual-in-residual）结构的网络,以提取赤潮边缘水体的高频特征;利用多感受野结构和坐标注意力机制捕获赤潮水体的位置语义信息,增强赤潮边缘水体的细节信息并抑制无用的特征。结果 在GF1-WFV （Gaofen1 wide field of view）赤潮数据集上的实验结果表明,所提出的RTDNet模型赤潮探测效果不仅优于支持向量机（support vector machine,SVM）、U-Net、Deep-Labv3+及HRNet （high-resolution network）等通用机器学习和深度学习模型,而且也优于赤潮指数法GF1_RI （Gaofen1 red tide index ）以及赤潮探测专用深度学习模型RDU-Net （red tide detection U-Net）,赤潮误提取、漏提取现象明显减少,F1分数在两幅测试图像上分别达到了0.905和0.898,相较于性能第2的模型DeepLabv3+提升了2%以上。而且,所提出的模型参数量小,仅有2.65 MB,约为DeepLabv3+的13%。结论 面向赤潮探测提出一种基于RIR结构的赤潮深度学习探测模型,通过融合多感受野结构和注意力机制提升了赤潮边缘探测的精度和稳定性,同时有效降低了计算量。本文方法展现了较好的应用效果,可适用于不同高分辨率卫星影像的赤潮探测。     

Vapor Phase Growth of Centimeter-Sized Band Gap Engineered Cesium Lead Halide Perovskite Single-Crystal Thin Films with Color-tunable Stimulated Emission

Single crystal metal halide perovskites thin films are considered to be a promising optical, optoelectronic materials with extraordinary performance due to their low defect densities. However, it is still difficult to achieve large-scale perovskite single-crystal thin films (SCTFs) with tunable bandgap by vapor-phase deposition method. Herein, the synthesis of CsPbCl_3(1–x)Br_3x SCTFs with centimeter size (1 cm × 1 cm) via vapor-phase deposition is reported. The Br composition of CsPbCl_3(1–x)Br_3x SCTFs can be gradually tuned from x = 0 to x = 1, leading the corresponding bandgap to change from 2.29 to 2.91 eV. Additionally, an low-threshold (≈23.9 µJ cm⁻²) amplified spontaneous emission is achieved based on CsPbCl_3(1–x)Br_3x SCTFs at room temperature, and the wavelength is tuned from 432 to 547 nm by varying the Cl/Br ratio. Importantly, the high-quality CsPbCl_3(1–x)Br_3x SCTFs are ideal optical gain medium with high gain up to 1369.8 ± 101.2 cm⁻¹. This study not only provides a versatile method to fabricate high quality CsPbCl_3(1–x)Br_3x SCTFs with different Cl/Br ratio, but also paves the way for further research of color-tunable perovskite lasing.     

一维水沙耦合模型参数敏感性分析——以2020年黄河下游洪水演进为例北大核心CSCD

一维水沙输移及河床冲淤模型中有较多理论上尚未解决的参数,而这些参数对一维模型精度的影响缺少定量分析。本文首先介绍了一维水沙耦合动力学模型,并将其应用于2020年汛期黄河下游的水沙过程模拟。计算结果表明:该模型能准确模拟水位和流量的变化,纳什效率系数基本大于0.85,均方根误差比相应的实测特征值小一个量级;计算沙峰虽然偏大,但计算的含沙量过程与实测过程整体符合,且模型计算的分河段冲淤量与断面地形法实测值较为接近。为分析模型中经验参数对模型计算结果的影响,以水文断面特征水沙参数的纳什效率系数作为目标函数,采用Sobol全局敏感性分析方法计算了各经验参数的敏感度指标。计算结果表明:糙率的总敏感度指标较高,是模型的高敏感参数;目标函数的选取对参数敏感度分析有一定影响,以含沙量纳什效率系数为目标函数时,挟沙力公式的系数及淤积时恢复饱和系数公式中指数的一阶敏感度和总敏感度指标较高;各参数组合对不同目标函数的影响差别较大,但对于同一目标函数存在影响作用相似的参数组合。全局敏感性分析方法刻画了模型参数的敏感性特征,有助于优化水沙数学模型参数取值。