期刊:
Journal of Materials Science: Materials in Electronics,2020年31(17):14421-14425 ISSN:0957-4522
通讯作者:
Wang, Haiou;Tan, Weishi
作者机构:
[Su, Kunpeng; Wang, Haiou; Zhang, Hui; Huang, Shuai; Huo, Dexuan] Hangzhou Dianzi Univ, Inst Mat Phys, Hangzhou 310018, Peoples R China.;[Tan, Weishi] Hunan City Univ, Coll Informat & Elect Engn, All Solid State Energy Storage Mat & Devices Key, Yiyang 413002, Peoples R China.;[Tan, Weishi] Nanjing Univ Sci & Technol, Dept Appl Phys, Key Lab Soft Chem & Funct Mat, Minist Educ, Nanjing 210094, Peoples R China.
通讯机构:
[Wang, Haiou; Tan, Weishi] H;[Tan, Weishi] N;Hangzhou Dianzi Univ, Inst Mat Phys, Hangzhou 310018, Peoples R China.;Hunan City Univ, Coll Informat & Elect Engn, All Solid State Energy Storage Mat & Devices Key, Yiyang 413002, Peoples R China.;Nanjing Univ Sci & Technol, Dept Appl Phys, Key Lab Soft Chem & Funct Mat, Minist Educ, Nanjing 210094, Peoples R China.
摘要:
Half-doped perovskite Manganese oxide has been widely studied because of its excellent properties such as colossal magnetoresistance (CMR) effect and charge-ordered (CO) phase separation. In this work, four Sm(0.5)Ca(0.5)MnO(3)samples with different particle sizes are prepared by high-temperature solid-state reaction and ball milling. The crystal structure of the samples is studied by X-ray diffraction (XRD). The Sm(0.5)Ca(0.5)MnO(3)sample is single phase, which belongs to orthorhombic structure. The surface morphology and particle size of the samples are examined by scanning electron microscope (SEM). The average particle size of the sample without ball milling is about 4 mu m. With ball milling time for 12 h, 24 h, and 36 h, the particle size decreases, and finally it reaches hundreds to tens of nanometers. This shows that ball milling is an effective way to control the particle size. The M-T curves and M-H hysteresis loops of the samples are measured by physical properties measurements systems (PPMS). The two M-T curves measured in the warming and cooling processes do not overlap for Sm(0.5)Ca(0.5)MnO(3)without ball milling, and the phenomenon of thermal hysteresis appears. Meanwhile, the M-T curve has a significant protuberance peak near 270 K. All of these indicate the CO behavior, whereas the particle size of Sm(0.5)Ca(0.5)MnO(3)decreases with different milling times (12-36 h) and the CO phase is suppressed gradually, which leads to the decrease of CO temperature, magnetization, remanence, and coercivity.
作者机构:
[张赛文; 冷潇泠; 张光富; 田野; 谭伟石] School of Information and Electronics Engineering, Hunan City University, Yiyang;413000, China;All-solid-state Energy Storage Materials and Devices Key Laboratory of Hunan Province, Hunan City University, Yiyang;[林丹樱; 于斌] Key Laboratory of Optoelectronic Devices and Systems of Ministry and Education and Guangdong Province, College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen;518060, China
通讯机构:
[Yu, B.] K;Key Laboratory of Optoelectronic Devices and Systems of Ministry and Education and Guangdong Province, China
摘要:
Road traffic is an important component of the national economy and social life. Promoting intelligent and Informa ionization construction in the field of road traffic is conducive to the construction of smart cities and the formulation of macro strategies and construction plans for urban traffic development. Aiming at the shortcomings of the current road traffic system, this article, on the basis of combining convolution neural network, situational awareness technology, database and other technologies, takes the road traffic situational awareness system as the research object, and analyzes the information collection, processing, and analysis process of road traffic situational awareness system. Convolutional neural networks (CNN), region-CNN (R-CNN), fast R-CNN, and faster R-CNN are used for vehicle class classification and location identification in road image big data. The deep convolutional neural network model based on road traffic image big data was further established, and the system requirements analysis and system framework design and implementation were carried out. Through the analysis and trial of actual cases, the results show the application effect of the realized road traffic situational awareness system, which provides a scientific reference and basis for the establishment of modern intelligent transportation system.
作者机构:
[文兵; 邓杨保; 张赛文; 邓曙光; 张光富] All-Solid-State Energy Storage Materials and Devices Key Laboratory of Hunan Province, College of Information and Electronic Engineering, Hunan City University, Yiyang;413002, China;[韦家谋] Key Laboratory for Micro-/Nano-Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha;410082, China;[文兵] 413002, China<&wdkj&>Key Laboratory for Micro-/Nano-Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha
通讯机构:
[Deng, Y.] A;All-Solid-State Energy Storage Materials and Devices Key Laboratory of Hunan Province, College of Information and Electronic Engineering, Hunan City University, Yiyang, China
摘要:
针对机载外辐射源雷达传统微弱目标检测方法运算量大、机动目标探测能力下降的问题,提出了机载外辐射源雷达微弱机动目标检测新方法.该方法首先对均匀分段、匹配滤波后的两通道信号进行参考信号多普勒补偿,并结合DPCA(Displaced Phase Center Antenna)技术实现杂波对消;然后采用Keystone变换校正径向速度引起的距离走动;最后应用匹配补偿函数解决径向加速度导致的目标能量分散问题.仿真结果表明,所提方法在Keystone变换之前抑制了杂波,克服了各通道进行Keystone变换导致的复杂度大的问题;匹配补偿函数的应用提高了机动目标的探测能力.
摘要:
Inferring potential associations between microRNAs (miRNAs) and human diseases can help people understand the pathogenesis of complex human diseases. Several computational approaches have been presented to discover novel miRNA-disease associations based on a top-ranked association model. However, some top-ranked miRNAs are not easily used to reveal the association between miRNAs and diseases. This study aims to infer miRNA-disease relationship by identifying a functional module. We first construct a miRNA functional similarity network derived from a disease similarity network and a known miRNA-disease relationship network. We then present an improved K-means (i.e., IK-means) algorithm to detect miRNA functional modules and used 243 diseases to validate the performance of our proposed method. Experimental results indicate that the performance of IK-means is better compared with classical K-means algorithms. Case studies on some functional modules further demonstrate the applicability of IK-means in the identification of new miRNA-disease associations.
期刊:
Journal of Luminescence,2020年228:117636 ISSN:0022-2313
通讯作者:
Zhou, Weiping;Tan, Weishi
作者机构:
[Chen, Guibin; Cheng, Ju; Zhai, Zhangyin; Ma, Chunlin] Huaiyin Normal Univ, Sch Phys & Elect Elect Engn, Huaian 223001, Peoples R China.;[Zhou, Weiping] Nanchang Univ, Sch Mat Sci & Engn, Nanchang 330031, Jiangxi, Peoples R China.;[Tan, Weishi] Hunan City Univ, Coll Informat & Elect Engn, All Solid State Energy Storage Mat & Devices Key, Yiyang 413002, Peoples R China.;[Wang, Xiaoxiong; Wang, Xingyu; Ma, Chunlin] Nanjing Univ Sci & Technol, Sch Sci, Nanjing 210094, Peoples R China.
通讯机构:
[Zhou, Weiping] N;[Tan, Weishi] H;Nanchang Univ, Sch Mat Sci & Engn, Nanchang 330031, Jiangxi, Peoples R China.;Hunan City Univ, Coll Informat & Elect Engn, All Solid State Energy Storage Mat & Devices Key, Yiyang 413002, Peoples R China.
摘要:
Chaotic particle swarm optimization algorithm is improved by incorporating antibody concentration, adaptive propagation, optimization mechanism of the multi-population evolution strategy, elite particles chaotic traversal mechanism and constraint processing mechanism. In this paper, an improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed. The performance of several algorithms has been compared by multimodal function, functions with high dimensional and complex constraints, bi-level programming function and a classic example of traffic network optimization. The experimental results prove that the proposed algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem.
摘要:
A fundamental problem facing deep neural networks is that they require a large amount of data to keep the system efficient in complex applications. Promising results of this problem are made possible by using techniques such as data enhancement or transfer learning in large data sets. However, when the application provides limited or unbalanced data, the problem persists. In addition, the number of false positives generated by deep model training has a significant negative impact on system performance. This study aims to solve the problem of false positives and class imbalances by implementing an improved filter library framework for Cole pest identification. The system consists of three main units: First, the primary diagnostic unit (boundary box generator) generates a bounding box containing the location of the infected area and class. Then, the promising box belonging to each category is used as an input to the secondary diagnostic unit (CNN filter bank) for verification. In the second unit, the misclassified samples are filtered by training for each category of independent CNN classifiers. The result of the CNN filter bank is to determine if a target belongs to the category because it is detected (true) or no (false), otherwise. Finally, an integrated unit combines the information of the autonomous unit and the secondary unit in the future while maintaining a true positive sample and eliminating false positives of misclassification in the first unit. By this implementation, the recognition rate of this method is about 96%, which is 13% higher than our previous work in the complex task of Cole disease and pest identification. In addition, our system is able to handle false positives generated by bounding box generators and class imbalances that occur on data sets with limited data.