检索结果-内蒙古大学图书馆

IET International Radar Conference 2018, IRC 2018

作者： Li, Yinchuan Zhao, Yikun Xiang, Yin Ding, Zegang Liu, Haibo Liu, Quanhua Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Wall-penetrating radar (WPR) is the device to image very small objects buried inside the wall. The small objects usually a few millimetres, whereas the bandwidth of WPR limits its resolution up to several centimetres. As a result, the target imaging result of the traditional method is 'defocused' in the range direction. To solve this problem, a near-field three-dimensional imaging algorithm based on matching filtering with multiple reference signals and selection processing is proposed. Instead of obtaining a focused image via coherent superposition processing, a focused image is determined by selecting an image from a plurality imaging results of different reference depths according to the minimum entropy criterion. The proposed algorithm is validated by imaging the computer simulation technology simulation data and the measured data. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Radar imaging

来源：评论

学校读者我要写书评

暂无评论

Prediction of Drug-Target Interactions with CNNs and Random Forest 16th

Prediction of Drug-Target Interactions with CNNs and Random ...

引用

16th International Conference on Intelligent Computing, ICIC 2020

作者： Lin, Xiaoli Xu, Minqi Yu, Haiping Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China School of Computer Huanggang Normal University Huanggang438000 China

ISBN: (纸本)9783030608019

Drug molecules interact with target proteins to influence the pharmacological action of the target to achieve the phenotypic effect, which can facilitate the identification of novel targets for current drug. Traditional biological experiments for discovering new drug-target interactions are expensive and time-consuming. Therefore, it is crucial to develop new prediction methods for identifying potential drug-target interactions. Computing methods have been increasing developed which can quickly and effectively predict drug-target interactions. In particular, machine learning methods have been widely used due to high predictive performance and computational efficiency. This paper first uses MACCS substructure fingerings to encode the drug molecules, then uses CNNs to extract the biological evolutionary information of target protein sequences, and finally uses random forest algorithm to predict drug-target interactions. Four datasets of drug-target interactions including Enzymes, Ion Channels, GPCRs and Nuclear Receptors, are independently used for building models with random forest. The results demonstrate our proposed method has a general compatibility, which is effective and feasible to predict drug-target interactions. © 2020, Springer Nature Switzerland AG.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Smart noise jamming suppression method based on fast fractional filtering

Smart noise jamming suppression method based on fast fractio...

引用

IET International Radar Conference 2018, IRC 2018

作者： Han, Bowen Yang, Xiaopeng Wu, Xuchen Li, Shuai Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Smart noise jamming based on digital radio frequency memory (DRFM) has both oppressive jamming effect and deceptive jamming effect, and it is difficult to be effectively suppressed by traditional anti-jamming methods. In order to solve this problem, an anti-jamming method for linear frequency modulation (LFM) pulse compression radar system is proposed. The proposed method is combined with fractional Fourier transform (FrFT) and fractional domain filtering strategy. Here, first, the FrFT transform is performed on the received signal, and the optimal transform angle is calculated by calculating the fractional fourth-order origin moment. Then, the narrow-band filter is performed in the fractional domain to extract the target signal and suppress the jamming signal. Finally, fractional Fourier inverse transform is done to restore the target signal. The simulation results show that the method not only can effectively suppress smart noise jamming but also can accurately detect the position of the target. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

Aircraft detection in Remote Sensing image for Space-borne platform

Aircraft detection in Remote Sensing image for Space-borne p...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Tang, Wei Jun, Baozhao Tang, Linbo Pan, Yu Jin, Dongling Quan, Zhengpiao Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781728123455

Aircraft detection in optical remote sensing image is an important research direction in the field of remote sensing. The existing detection methods are difficult to achieve satisfactory results. Traditional detection methods are low robustness, due to manual feature modeling are difficult and subject to background interference;The deep learning target detection method, which improves the detection performance at the cost of complexity improvement, cannot be widely used in space-borne platforms limited resources. In view of the above problems, this paper proposes an aircraft target deep learning detection method of lightweight and multi-scale features. On the basis of the multi-scale target detection framework (SSD), the method firstly uses the dense connection structure and the double convolution channels to form the basic backbone networks with feature reuse and high computational efficiency. To improve the detection performance of the small aircraft target, the basis backbone network connects a residual module and deconvolution to compose the multi-scale feature fusion detection module. Compared with the current classical deep learning object detection methods, the experimental results show that the proposed method has the advantages of maintaining low computational complexity and achieving high detection accuracy. © 2019 IEEE.

关键词： Aircraft detection

来源：评论

学校读者我要写书评

暂无评论

Modeling and Analysis of Pumping Cell of Nox Sensor - Part Ii: Nox Pumping Cell

SSRN

引用

SSRN 2022年

作者： Wang, Zhen Deng, Zhong-Hua Wang, Jie Lin, Wei-Xun Fu, Xiao-Wei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Imaging Processing and Intelligent Control Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen518055 China Lambda Company Jiangsu Changzhou213100 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

To improve the performance of the NOx sensor, a combined model based on the electrochemical model and the diffusion model is developed for the NOx pumping cell of the NOx sensor. Considering that a small amount of oxygen enters the third chamber and part of NO is decomposed in the second chamber, the electrochemical model simultaneously considers the reactions of NO and oxygen at cathode, and the diffusion model additionally considers the diffusion of oxygen and the decomposition of NO. To validate the model at different gas concentrations, the sensor is mounted on the designed test rig. Then, the simulation results are compared with experimental data from the test rig and good agreement is obtained. In addition, three key parameters of the model of the NOx pumping cell are analyzed. The values and changing trends of these three parameters are given in the paper. © 2022, The Authors. All rights reserved.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

SAR Image Scattering Center Estimation via Atomic Norm Minimization 6

SAR Image Scattering Center Estimation via Atomic Norm Minim...

引用

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Xi, Ying Liu, Haibo Wang, Yanhua Li, Yang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728129129

Scatter center matching is one of the choke points of radar automatic target recognition (RATR) based on Synthetic Aperture Radar (SAR). Accuracy of scattering center extraction is an important factor affecting target recognition performance. This paper proposes a SAR images scattering center estimation method based on atomic norm minimization (ANM) model. This method models the scattering center estimation problem as a two-dimensional line spectrum estimation problem, then the estimation is performed in a continuous domain via ANM. The experimental results indicate the superiorities of the proposed method in terms of accuracy and robustness. © 2019 IEEE.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Distorted SAR target recognition with virtual SVM and AP-HOG feature

Distorted SAR target recognition with virtual SVM and AP-HOG...

引用

IET International Radar Conference (IET IRC 2020)

作者： D. Yao Z. Liu F. Li Y. Li School of Information and Electronics Beijing Institute of Technology Radar Research Laboratory Beijing 100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing 100081 China

Synthetic aperture radar (SAR) plays an indispensable role in national defence and civil fields due to its unique advantages. Although many methods have been proposed, the recognition to SAR images still faces many difficulties, especially to distorted images. This paper first improves the morphological methods attribute profiles (APs) which is widely employed with optical, and hyperspectral images in particular. We combine the APs method with histogram of oriented gradients (HOG) feature (called AP-HOG) to make it suitable for SAR images. Then, the SVM model is used to identify the labelled samples (i.e., Support Vectors) that are closest to the separating hyperplane with maximum margin. We generate different degrees of distorted images based on support vectors, and use the distorted image and the support vector as a new training set for retraining to improve the recognition effect of the distorted image. Finally, we perform experimental verification based on the MSTAR dataset, which proves the effectiveness of the proposed method.

关键词： feature extraction synthetic aperture radar support vector machines radar imaging image recognition object detection image classification

来源：评论

学校读者我要写书评

暂无评论

SAR target recognition combining class information and classification hyperplane

SAR target recognition combining class information and class...

引用

IET International Radar Conference (IET IRC 2020)

Automatic target recognition (ATR) in synthetic aperture radar (SAR) images plays an important role. An important step in SAR target recognition is to transform the raw data to a feature space with the property of within-class compactness and between-class separation. However, due to the problem of target aspect angle sensitivity in SAR images, the difference of some targets belonging to the same class in different poses is greater than the difference of targets belonging to different class in similar poses, which often causes misjudgement. To solve this problem, we first use the twin support vector machine (TWSVM) to obtain the classification hyperplane of each class. Then, based on the obtained hyperplane, the projection matrix is constructed by fusing the prior class information of the target. In this way, the sample can be close to the classification hyperplane of the same class and distant from the classification hyperplane of a different class. This algorithm can improve TWSVM's ability to classify SAR images. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) database. The results verify the effectiveness of the proposed algorithm.

关键词： support vector machines image recognition pattern classification radar target recognition feature extraction image classification radar imaging synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Retraction Note: Salient object detection using a covariance-based CNN model in low-contrast images

引用

Neural Computing and Applications 2024年第19期36卷 11687-11687页

作者： Mu, Nan Xu, Xin Zhang, Xiaolong Zhang, Hong School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

来源：评论

学校读者我要写书评

暂无评论

Novel acceleration compensation method for highly squint mode SAR with curve trajectory

Novel acceleration compensation method for highly squint mod...

引用

IET International Radar Conference 2018, IRC 2018

作者： Zhou, Zhichao Ding, Zegang Zeng, Tao Li, Gen Li, Linghao School of Information and Electronics Beijing Institute Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

Due to the influence of air currents and the control of the platform's own power system, acceleration is often present in the motion platform equipped with synthetic aperture radar (SAR) system, which causes the actual trajectory of the platform to be curved. When the acceleration is large or the azimuth accumulation time is long, a large envelope error or phase error will be caused. This problem will be more obvious in highly squint mode SAR. On the other hand, the acceleration may also cause the situation where the absolute value of the Doppler frequency rate is extremely small, resulting in the azimuthal time-bandwidth product that is too small to satisfy the requirements of the stationary phase principle. To solve the above problems, a novel acceleration compensation method based on inertial navigation information is proposed in this article. By integrating the acceleration and velocity information provided by the inertial navigation system, the position information of maneuvering platform is obtained. Then the actual trajectory of the platform is constructed. On this basis, the envelope and phase errors caused by the acceleration are estimated. The results of real data processing verify the correctness of the analysis and the effectiveness of the proposed method. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Acceleration

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：