检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group Pladema Institute Conicet Unicen Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria

Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs. This paper provides insights about PALM, our open fundus imaging dataset for pathological myopia recognition and anatomical structure annotation. Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment. In addition, this paper elaborates on other details such as the labeling process used to construct the database, the quality and characteristics of the samples and provides other relevant usage notes. © 2023, CC BY-NC-ND.

关键词： Photography

来源：评论

学校读者我要写书评

暂无评论

Bag-of-Features Model for ASD fMRI Classification using SVM

Bag-of-Features Model for ASD fMRI Classification using SVM

引用

Communications Technology and Computer Science (ACCTCS), Asia-Pacific Conference on

作者： Md Shale Ahammed Sijie Niu Md Rishad Ahmed Jiwen Dong Xizhan Gao Yuehui Chen Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan Shandong China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665447034

Autism spectrum disorder (ASD) is an abstruse brain disorder in neuroscience research, which lead by challenges of social interactions, speech and nonverbal communication, and repetitive behaviors. Diagnosis of ASD is mostly based on behavioral analysis, which is time-consuming and depends on patient cooperation and examiner expertise. Recently, machine learning(ML) based algorithms are applied for ASD identification. However, there are some limitations using ML approaches such as investigation with functional magnetic resonance imaging (fMRI), depend on region-based analysis, and handling with the big dataset. In this work, we proposed a novel architecture based on the Bag-of-Features model for ASD classification to overcome these challenges. Firstly, we preprocess the images to extract the speeded-up robust features (SURF) from the selected feature point locations. The Bag-of-Feature extraction procedures include feature concatenation, select the most robust feature, and convert to feature vector. After that, we employ K-Means clustering to create a word visual vocabulary from the SURF vector. Then, we encode the Bag-of-Features by adopting coding and quantization techniques to get each class's indexed database. We prefer most tremendous machine learning methods SVM as ASD classifier. Finally, we independently evaluate our proposed architecture's performance using three different datasets from different fields, including ABIDE fMRI preprocessed images and subject's face images. In our experiments, weigh against other state-of-the-art methods that our ML classifiers with Bag-of-Feature extractors reinforce in medication and clinical purposes of ASD.

关键词： Support vector machines Autism Vocabulary Visualization Quantization (signal) Neuroscience Machine learning

来源：评论

学校读者我要写书评

暂无评论

Design and implementation of temperature sensor data acquisition system based on multithread 9

Design and implementation of temperature sensor data acquisi...

引用

9th International Conference on computing and Pattern Recognition, ICCPR 2020

作者： Zhang, Wenjing Liu, Pengyu Chen, Shanji Ma, Ying Jia, Kebin Wang, Yanming Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China School of Physics and Electronic Information Engineering Qinghai Nationalities University Xining810000 China

ISBN: (纸本)9781450387835

The sounding temperature sensor is an important part of the high-altitude image detector. In the face of a complex atmospheric environment, it is not sufficient to only measure atmospheric humidity data and atmospheric pressure data. At the same time, it is necessary to obtain the collected information of the sounding temperature sensor in combination with the current high-altitude environment to achieve three-dimensional space weather data measurement. Aiming at the problems of complicated collection process, low efficiency and large number of terminal equipments existing in the existing manual polling collection methods of temperature sensors, this paper proposes and designs a set of temperature sensor collection system based on multithreading. First, design a complete and reasonable temperature sensor collection process based on the existing equipment. Second, propose a multi-threaded collection strategy to overcome the problems of the current manual round-robin collection mechanism, and then initialize the device, follow the websocket communication protocol, and add an information format judgment mechanism, Complete the stable and accurate transmission of data, and finally realize the automatic prompt function when a terminal device controls 8 thermostats with different temperatures in a stable state, reducing manual participation, improving collection efficiency, and comparing it to find that the current data collection is more stable and the balance state is judged It is accurate and can be used for the deployment of high-altitude image detectors. © 2020 ACM.

关键词： Data acquisition

来源：评论

学校读者我要写书评

暂无评论

A recognition algorithm suitable for complex scenes and various types of license plates 9

A recognition algorithm suitable for complex scenes and vari...

引用

9th International Conference on computing and Pattern Recognition, ICCPR 2020

作者： Li, Yaoyao Chen, Shanji Liu, Pengyu Jia, Kebin Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China School of Physics and Electronic Information Engineering Qinghai Nationalities University Xining810000 China

ISBN: (纸本)9781450387835

With the acceleration of intelligent transportation in the new era, license plate recognition technology has become more and more extensive. Although the existing license plate recognition technology can show good performance for specific scenes and traditional license plate recognition, it has poor recognition robustness and applicability in complex external environments such as arbitrary shooting angles, different lighting conditions, and different picture quality. To solve the above problems, this paper proposes an improved recognition algorithm suitable for complex scenes and various types of license plates. First, the SSD model is used for multi-target vehicle detection;the convolutional neural network (CNN) is further used for license plate location and correction;finally, the YOLOv2 model with modified output and reduced pooling layer is designed to realize the license plate character recognition. The algorithm in this paper is carried out on the surveillance image data of a road in a block of beijing, which contains 3016 images with a total of 5315 vehicles to be detected. The experimental results show that the accuracy rate of the license plate recognition of the proposed method reaches 95.66%, and it has considerable detection performance for traditional license plates and new energy license plates. Compared with the classic algorithm HyperLPR, the recognition accuracy of the proposed algorithm in complex scenarios is improved by 13 %. © 2020 ACM.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Nonlinear Correction Based on Depth Neural Network 1

引用

6th International Conference on Artificial intelligence and Security,ICAIS 2020

作者： Wang, Yanming Jia, Kebin Liu, Pengyu Yang, Jiachun Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China Tianjin Huayun Tianyi Special Meteorological Detection Technology Co. Ltd. Tianjin300392 China

ISBN: (数字)9789811580833

ISBN: (纸本)9789811580826

With the global climate change, the high-altitude detection is more and more important in the climate prediction. Due to the interference of the measured objects and the measured environment, the input and output characteristic curve of the pressure sensor will shift, resulting in nonlinear error. Aiming at the difficulty of nonlinear correction of pressure sensor and the low accuracy of correction results, depth neural network model was established based on wavelet function, and Levenberg-Marquardt algorithm is used to update network parameters to realize the nonlinear correction of pressure sensor. The experimental results show that compared with the traditional neural network model, the improved depth neural network not only accelerates the convergence rate, but also improves the correction accuracy, meets the error requirements of upper-air detection, and has a good generalization ability, which can be extended to the nonlinear correction of similar sensors. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Fusion Tree Network for RGBT Tracking

Fusion Tree Network for RGBT Tracking

引用

IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Zhiyuan Cheng Andong Lu Zhang Zhang Chenglong Li Liang Wang School of Computer Science and Technology Anhui University Hefei China Center for Research on Intelligent Perception and Computing NLPR CASIA Beijing China University of Chinese Academy of Sciences Beijing China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei China School of Artificial Intelligence Anhui University Hefei China

ISBN: (数字)9781665463829

ISBN: (纸本)9781665463836

RGBT tracking is often affected by complex scenes (i.e., occlusions, scale changes, noisy background, etc). Existing works usually adopt a single-strategy RGBT tracking fusion scheme to handle modality fusion in all scenarios. However, due to the limitation of fusion model capacity, it is difficult to fully integrate the discriminative features between different modalities. To tackle this problem, we propose a Fusion Tree Network (FTNet), which provides a multi-strategy fusion model with high capacity to efficiently fuse different modalities. Specifically, we combine three kinds of attention modules (i.e., channel attention, spatial attention, and location attention) in a tree structure to achieve multi-path hybrid attention in the deeper convolutional stages of the object tracking network. Extensive experiments are performed on three RGBT tracking datasets, and the results show that our method achieves superior performance among state-of-the-art RGBT tracking models.

关键词： Fuses Convolution Surveillance Aggregates Benchmark testing Object tracking Noise measurement

来源：评论

学校读者我要写书评

暂无评论

Model Free Adaptive Predictive Control of Desulfurization Slurry pH Based on CPS Framework

引用

Journal of beijing Institute of Technology 2020年第4期29卷 544-555页

作者： Jian Liu Xiaoli Li Kang Wang Fuqiang Wang Guimei Cui Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Ministry of EducationBeijing 100124China Beijing Advanced Innovation Center for Future Internet Technology Beijing University of TechnologyBeijing 100124China Technology Research Center Shenhua Guohua(Beijing)Electric Power Research Institute CorporationBeijing 100025China School of Information Engineering Inner Mongolia University of Science and TechnologyBaotou 014010China

In order to improve the slurry pH control accuracy of the absorption tower in the wet flue gas desulfurization process,a model free adaptive predictive control algorithm for the desulfurization slurry pH which is based on a cyber physical systems framework is ***,aiming to address system characteristics of non-linearity and pure hysteresis in slurry pH change process,a model free adaptive predictive control algorithm based on compact form dynamic linearization is proposed by combining model free adaptive control algorithm with model predictive control ***,by integrating information resources with the physical resources in the absorption tower slurry pH control process,an absorption tower slurry pH optimization control system based on cyber physical systems is *** is turned out that the model free adaptive predictive control algorithm under the framework of the cyber physical systems can effectively realize the high-precision tracking control of the slurry pH of the absorption tower,and it has strong robustness.

关键词： wet flue gas desulfurization slurry pH cyber physical systems model free adaptive predictive control tracking control

来源：评论

学校读者我要写书评

暂无评论

Semantic Segmentation of Oil Well Sites Using Sentinel-2 Imagery

Semantic Segmentation of Oil Well Sites Using Sentinel-2 Ima...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Hao Wu Hongli Dong Zhibao Wang Lu Bai Fengcai Huo Jinhua Tao Liangfu Chen Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University School of Computer and Information Technology Northeast Petroleum University Daqing China Bohai-Rim Energy Research Institute Northeast Petroleum University Qinhuangdao China School of Computing Ulster University Belfast UK State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

The number and geographical location of oil well sites can reflect the local oil production situation and there is a growing interest in automatically identifying oil well sites from remote sensing images. Traditionally, visual interpretation was employed to extract oil well sites locations from remotely sensing images. However, this approach is time-consuming and heavily dependent on domain experts. Advancements in remote sensing satellite technology and the widespread use of deep learning algorithms have enabled the automated extraction of oil well sites from remote sensing images. In this paper, we established the Northeast Petroleum University Oil Well Sites Dataset Version 1.0 (NEPU-OWS V1.0), and to evaluate its usability by comparing several different deep learning models based on semantic segmentation algorithms for optical remote sensing images. Experimental results show that current advanced deep learning models achieve high accuracy on this dataset, demonstrating great potential for remote sensing detection in oil well sites.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Model of Information Circulation and Transmission Network of Grid Cells in Entorhinal Cortex and Place Cells in the Hippocampus of Rat

A Model of Information Circulation and Transmission Network ...

引用

IEEE Joint International Information Technology and Artificial intelligence Conference (ITAIC)

作者： Naigong Yu Hejie Yu Lin Wang Yishen Liao Chunlei Yin Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Biological studies have found that the grid cells in the entorhinal cortex of the rat brain structure and the place cells in the hippocampus structure have a mutual input and output cyclic relationship in the process of information transmission, from entorhinal cortex-hippocampus information transfer loop. The traditional neural network model from grid cells to place cells is not enough to satisfy the information transmission relationships, ignores the information transfer from place cells to grid cells, to solve this problem, a neural network model based on grid cells in entorhinal cortex and place cells in hippocampus information transfer loop is proposed. The simulation results show that the model can simulate the firing characteristics of place cells and grid cells.

关键词： Biological system modeling Cells (biology) Information processing Rats Brain modeling Integrated circuit modeling Biological neural networks

来源：评论

学校读者我要写书评

暂无评论

Research on the Prediction Model of key Personnel's Food Crime Based on Stacking Model Fusion

Research on the Prediction Model of Key Personnel's Food Cri...

引用

International Conference on Informative and Cybernetics for Computational Social systems (ICCSS)

作者： Yupeng Zhai Xiaoli Li Kang Wang Faculty of Information Technology Beijing University of Technology Beijing China Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing University of Technology Beijing China

ISBN: (纸本)9781665402460

Crime prediction is of great significance to the food safety defense work of major events. The traditional crime prediction depends on the experience of police officers, which is highly subjective and can not be predicted in advance. This paper analyzes and processes food safety police data, combines the characteristics of food crimes, uses stacking model fusion method to predict the food crime tendency of key personnel, and verifies the model through the Recall Rate. The results show that the integrated learning model has high accuracy and can effectively predict the food crime tendency of key personnel.

关键词： Analytical models Data analysis Machine learning algorithms Law enforcement Computational modeling Stacking Predictive models

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：