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Automatic detection of pneumonia in chest X-ray images using cooperative convolutional neural networks 1

2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

作者： Wang, Kun Zhang, Xiaohong Huang, Sheng Chen, Feiyu School of Big Data and Software Engineering Chongqing University Chongqing401331 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400044 China

ISBN: (数字)9783030317232

ISBN: (纸本)9783030317225

Chest X-ray images are currently the best available visual media for diagnosing pneumonia, which plays a crucial role in clinical care. Medical images diagnosing can be error-prone for inexperienced radiologists, while tedious and time-consuming for experienced radiologists. To address these issues, we study automatically detect pneumonia in Chest X-ray images. However, this task exists several challenges. First, abnormal regions of pneumonia are difficult to identify due to the noise interference from other tissue and lesions. Second, the features of lung regions are usually essential information for diagnosis. With the pneumonia disease happens in lung areas, only training CNNs using global image may be affected by the irrelevant noisy regions. Third, the appearance of pneumonia in X-ray images is often vague, can overlap with other diagnoses. To cope with these challenges, we first introduce a lung segmentation network, which segments the lung from the original images. Then, develop a feature extraction model, which incorporates global and local features for pneumonia classification. Finally, build a cooperative learning framework, which merges bounding boxes (localization of pneu-monia) from two cooperative models. We demonstrate the effectiveness of our proposed methods on the Kaggles RSNA Pneumonia Detection Challenge dataset and reaches excellent performance with the level of the top 1% in this competition. © Springer Nature Switzerland AG 2019.

关键词： Diagnosis

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Efficient scheduling with intensive in-memory file accesses considering bandwidth constraint on memory bus 15th

Efficient scheduling with intensive in-memory file accesses ...

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Wu, Lin Zhuge, Qingfeng Sha, Edwin H.-M. Sun, Zhilong Key Laboratory of Dependable Service Computing in Cyber Pyhsical Society Ministry of Education Chongqing400044 China College of Computer Science Chongqing University Chongqing400044 China

ISBN: (纸本)9783319271217

The latest trend in high performance computing and big data applications inspires new solutions of in-memory filesystems to achieve high throughput of file data accesses. However, new concerns arise because intensive in-memory file accesses can cause contention problem on memory bus. With existing schedulers, such as Complete Fair Scheduler (CFS) in Linux system, memory access intensive processes can excessively occupy memory bandwidth. As a result, the performance of a set of running processes is degraded. In this paper, we propose a scheduling algorithm to consider memory bandwidth consumption during scheduling and improve the completion time of a given working set. We first present the data to show that CFS can not efficiently handle a set of running processes involving intensive in-memory file accesses. Then, a heuristic algorithm is proposed to solve the scheduling problem with bandwidth consumption and execution time consideration. The scheduling algorithm is implemented and extensively experimented in Linux system. The experimental results show that the completion time is reduced up to 33.3% compared with that generated by Linux scheduler CFS. © Springer International Publishing Switzerland 2015.

关键词： Bandwidth

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Deep Convolutional Neural Networks with Random Subspace Learning for Short-term Traffic Flow Prediction with Incomplete Data

Deep Convolutional Neural Networks with Random Subspace Lear...

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International Joint Conference on Neural Networks

作者： Shijie Liao Jing Chen Jiaxin Hou Qingyu Xiong Junhao Wen Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China

Traffic flow prediction is a fundamental component in intelligent transportation systems. However, many existing prediction models endure several shortages. Most of the methods are constructed as a shallow model, which is difficult to reveal the intrinsic spatio-temporal relations embedded in traffic raw data. Moreover, the separation of feature learning and predictor learning brings a sacrifice of model performance. Then the hand designed features are difficult to be tuned appropriately. Finally, few existing methods consider the incomplete data problem which is in fact very severe for practical application. In this work, we develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that integrates random subspace learning and ensemble learning on deep convolutional neural networks. The proposed model takes the traffic flow data as an image, and considers both exploring spatio-temporal correlations in the unified architecture and the incomplete data problem. The experimental results, using traffic data originated from the California Freeway Performance Measurement System (PeMS), corroborate the effectiveness of the proposed approach compared with the state of the art.

关键词： Correlation Predictive models Data models Autoregressive processes Computational modeling Convolutional neural networks Task analysis

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Robust Small Target Detection under Intense Noise Based on Extreme Learning Machine 2

Robust Small Target Detection under Intense Noise Based on E...

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2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

作者： Liu, Depeng Li, Zhengzhou Che, Peng Chen, Cheng Cao, Lei Chongqing University College of Communication Engineering Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China Chinese Academy of Sciences Key Laboratory of Beam Control Chengdu China

ISBN: (纸本)9781538618035

T211 robust detection of dim small targets is the key problem in infrared search and track systems. However, the intense noise in infrared image has similar appearance and intensity distribution with the dim small target. This may lead to terrible probabilities of detection and serious false alarm rates for traditional small target detection methods. To cope with the disturbance of intense noise, a novel small target detection method based on the cascade sparse histogram feature and extreme learning machine is proposed in this paper. Firstly, top-hat transformation based pre-processing is applied on the infrared image to eliminate most of the background clutter. The top-hat based pre-processing can reduce the computation burden for the subsequent extreme learning machine based classification phase, however, some intense noise might still remain in the pre-processed image. Then, a novel descriptive feature namely the cascade sparse histogram is proposed to describe the characteristics of the target and the noise. Finally, the feature vectors of target and noise are discriminated by the extreme learning machine so the target could be detected. Extensive experiments show that the proposed method not only has outstanding small target detection performance under different backgrounds but also works more stably against intense noise compared with conventional baseline methods. © 2018 IEEE.

关键词： Chemical detection

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MoNet3D: Towards accurate monocular 3d object localization in real time 37

MoNet3D: Towards accurate monocular 3d object localization i...

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37th International Conference on Machine Learning, ICML 2020

作者： Zhou, Xichuan Peng, Yicong Long, Chunqiao Ren, Fengbo Shi, Cong Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China Arizona State University TempeAZ United States

ISBN: (纸本)9781713821120

Monocular multi-object detection and localization in 3D space has been proven to be a challenging task. The MoNet3D algorithm is a novel and effective framework that can predict the 3D position of each object in a monocular image and draw a 3D bounding box for each object. The MoNet3D method incorporates prior knowledge of the spatial geometric correlation of neighbouring objects into the deep neural network training process to improve the accuracy of 3D object localization. Experiments on the KITTI dataset show that the accuracy for predicting the depth and horizontal coordinates of objects in 3D space can reach 96.25% and 94.74%, respectively. Moreover, the method can realize the real-Time image processing at 27.85 FPS, showing promising potential for embedded advanced drivingassistance system applications. Our code is publicly available at https://***/CQUlearningsystemgroup/YicongPeng. © 2020 by the Authors All rights reserved.

关键词： Deep neural networks

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Bus penetration rate estimation of road segment using bus GPS data 30

Bus penetration rate estimation of road segment using bus GP...

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30th Chinese Control and Decision Conference, CCDC 2018

作者： Sun, Dihua Chen, Qiuqu Zhao, Min Zheng, Linjiang Liu, Weining Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing China College of Automation Chongqing University Chongqing400044 China College of Computer Science Chongqing University Chongqing400044 China

ISBN: (纸本)9781538612439

To realize the estimation of bus penetration rate in road segment using bus GPS data, a method is proposed in this paper to estimate bus penetration rate in single signal cycle, according to the bus position at the end of red phase at a signalized intersection. Sum of squares for factor A (SSA) of bus penetration rate and time function in different signal cycle length are selected to characterize the accuracy and real-time quality of the estimation, on the basis, objective function is constructed to find appropriate signal cycle length as the time window. Aiming at the possible abnormal points in single cycle bus penetration rate, quartile method was used to eliminate them. In the end, simulation tests were carried out under different traffic flow and bus penetration rates, the results show that the proposed method in this paper has better performance when traffic flow is higher than 600veh/h, and the bus penetration rate is higher than 5%. On the same time, a field test was conducted in an arterial road of Chongqing, the results show that the estimation method proposed in this paper can estimate the bus penetration rate of road segment effectively. © 2018 IEEE.

关键词： Estimation Roads Global Positioning System Real-time systems Linear programming Queueing analysis Data models

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Assessment of security and vulnerability of home wireless networks

Assessment of security and vulnerability of home wireless ne...

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2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

作者： Stimpson, Thomas Liu, Lu Zhang, Jianjun Hill, Richard Liu, Weining Zhan, Yongzhao DISYS Research Group School of Computing and Mathematics University of Derby United Kingdom Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing China School of Computer Science and Telecommunication Engineering Jiangsu University Jiangsu China

ISBN: (纸本)9781467300223

Wireless networks are being used in homes far more extensively than ever before. With the growth of the Internet, coupled with wireless devices such as laptops and Wi-Fi enabled phones, their usage has increased greatly. This project discovered the security trends in the home wireless market by collecting data directly from the source. This information, when analysed, provided an overview of wireless local area networks and the lack of security used. The findings were then used to create a set of recommendations and how it is believed the current system is failing to provide the safety measures needed to ensure that users stay secure while utilizing wireless networks within the home. © 2012 IEEE.

关键词： Cryptography

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High-payload completely reversible data hiding in encrypted images by an interpolation technique

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Frontiers of Information Technology & Electronic Engineering 2017年第11期18卷 1732-1743页

作者： Di XIAO Ying WANG Tao XIANG Sen BAI MOE Key Laboratory of Dependable Service Computing in Cyber Physical Society College of Computer Science Chongqing University Chongqing 400044 China Department of lnformation Engineering Chongqing Communication Institute Chongqing 400035 China

We present a new high-payload joint reversible data-hiding scheme for encrypted images. Instead of embedding data in the encrypted image directly, the content owner first uses an interpolation technique to estimate whether the location can be used for embedding and generates a location map before encryption. Next, the data hider embeds the additional data through flipping the most significant bits （MSBs） of the encrypted image according to the location map. At the receiver side, before extracting the additional data and reconstructing the image, the receiver decrypts the image first. Experimental results demonstrate that the proposed method can achieve real reversibility, which means data extraction and image recovery are free of error. Moreover, our scheme can embed more payloads than most existing reversible data hiding schemes in encrypted images.

关键词： Encrypted image Data hiding Image recovery Real reversibility Interpolation

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Short-term load forecasting for electric vehicle charging stations based on time series distance measuring 13

Short-term load forecasting for electric vehicle charging st...

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2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)

作者： Shi Xin Qiu Lei Tian Li Liang Miaozhu Yi Lizheng Key Laboratory of Service Computing in Cyber Physical Society MOE College of Automation Chongqing University State Grid Chongqing Electric Power Company

ISBN: (纸本)9781509050369;9781509050352

To improve the efficiency and the precision of short-term load forecasting of the electric vehicle charging stations, this paper proposed a new algorithm for short-term load forecasting for electric vehicle charging stations based on time series distance measuring. Applying the editing distance to load the load sequence into the load segment and the time segment respectively to measure, so as to measure the distance between the load sequences accurately, And simplify the complexity of model which can improve the efficiency of prediction. Based on the actual historical load data of electric vehicle charging stations, the simulation analysis is given to verify that the proposed method has high prediction efficiency and accuracy.

关键词： Electric Vehicle Charging Station Short-term load forecasting Edit Distance Time Series distance measuring

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Identify Online Fraudster with Extended Cellular Automata

Identify Online Fraudster with Extended Cellular Automata

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IEEE/ACM Int'l Conference on & Int'l Conference on cyber, physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Ji Li Yueliang Xiao Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing China

ISBN: (纸本)9781479906314

Auction fraud and anomalies detection is proved to be a troublesome task. The fraudulent cliques can easily forge lots of unreal transactions, to improve their reputation and cheat the auction reputation system. By applying cellular automata reference to the transaction graph, which is constructed from users and their transaction feedbacks, we introduced an graphic model of cellular automata, which is named GCA, and a parallel analysis algorithm was implemented on Hadoop. Series of data preprocessing were performed beforehand, including data cleaning, selection and manual verification. By the definition of valid state transition rules, GCA functioned well in state prediction under the influence of its neighbors. Finally, several groups of experiments were carried out, and the results demonstrated that GCA is both effective and efficient, as it was able to predict the credible state of the tens of thousands of traders in less than one second.

关键词： Automata Graphics Algorithm design and analysis Accuracy Classification algorithms Social network services Prediction algorithms

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