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International Symposium on Autonomous Decentralized systems (ISADAS)

作者： Guanqiu Qi Zhiqin Zhu Matthew Haner Jaesung Sim Jian Sun Yi Chai Yinong Chen Li Yongfu Mathematics and Computer Information Science Mansfield University of Pennsylvania Mansfield PA 16933 USA College of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Electronic and Information Engineering Southwest University Chongqing 400715 China Ministry of Education Key Laboratory of Complex System Safety and Control (Chongqing University) Chongqing 400044 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287 USA

ISBN: (数字)9781728116723

ISBN: (纸本)9781728116730

In cloud computing, data is duplicated to prevent data loss. One way to achieve data consistency in such a distributed computing systems is to use a blockchain. Based on practical Byzantine fault tolerance (PBFT), a specific type of blockchain, this paper proposes a synchronous Byzantine fault tolerance (SBFT) algorithm that not only maintains data consistency, but also has much higher efficiency than other general blockchain algorithms. We provide experimental results that demonstrate the algorithm's data consistency, efficiency, and reliability.

关键词： Cloud computing Fault tolerance Fault tolerant systems Peer-to-peer computing

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Efficient Dense Spatial Pyramid Network for Lane Detection

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yinggang Li Xiaobo Lu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Lane detection separating the roads and positioning each lane is a significant task in Autonomous-driving field. Most of existing methods take lane detection as a semantic segmentation problem, and the methods based on encoder-decoder networks achieve robust performances than traditional methods with the disadvantages of large amounts of parameters and high computational cost. To improve efficiency and better adapt to adverse environments, we propose an EDSPnet by compiling the efficient dense module of depthwise dilated separable convolution (EDD module) and dense spatial pyramid (DSP) module. The two proposed modules enable our network to make full use of the contextual information from different feature maps when compared to a chain of layers. Different from symmetrical segmentation networks, we concentrate on the encoder block and a simple decoder is designed to match the input resolution. The decoder is complemented by two methods of upsampling: bilinear interpolation and deconvolution, which guarantee the accuracy and efficiency simultaneously. Our method is evaluated on the TuSimple dataset, and the experimental results show that our network has less parameters and is still efficient even compared with the state-of-the-art semantic segmentation networks.

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Vehicle and Driver Detection on Highway Based on Cascade R-CNN

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yifei Geng Xiaobo Lu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Vehicle and driver detection in the highway scene has been a research hotspot in the field of object detection in recent years, and it is still a challenging problem in the research of traffic order and road safety. In this paper, we propose a novel end-to-end vehicle and driver detection method named VDDNet which is based on Cascade R-CNN and SENet. By introducing FPN structure and SENet attention mechanism in the backbone, the ability of the model to learn effective features is enhanced. It can improve the accuracy of detection in difficult scenes such as weak light, partial occlusion, and low picture resolution. The test results based on the database of highway traffic vehicle and drivers constructed by the Jiangsu Provincial Public Security Department. It shows that the detection method has the AP rate of 91.3% and the Recall rate of 92.4%, which demonstrates the effectiveness of the proposed method in complex highway environments.

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Single Image Depth Level Estimation Using Dark Channel Prior

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IOP Conference Series: Materials Science and engineering 2018年第1期435卷

作者： Yichao Cao Xiaobo Lu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

In this paper, we present a simple image depth level estimation algorithm. From the dark channel prior theory, an estimate of the air transmittance in the image is calculated. In wild surveillance, the disparity in the image poses a huge challenge for smoke detection and other video analysis tasks. Appropriate depth level estimation provide significant prior knowledge for subsequent identification and detection. For landscape images, we can approximate the air transmittance to depth information for histogram analysis. The depth value is segmented by a multi-threshold segmentation algorithm, and the resulting image can be used for forest fireworks detection and the like. This method does not rely on samples and classifiers, and the algorithm does not require training. The final experimental results show that the depth level estimation of a single landscape image based on the dark channel prior can achieve good results.

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A Deep Learning Approach for Face Detection and Location on Highway

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IOP Conference Series: Materials Science and engineering 2018年第1期435卷

作者： Yang Zhang Peihua Lv Xiaobo Lu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Face detection and location technique is a hot research direction during recent years. Especially, driver face detection on highway is still a challenging problem in social safty deserving research. This paper proposes a novel algorithm based on the improved Multi-task Cascaded Convolutional Networks (MTCNN) and Support Vector Machine (SVM) to realize accurate face region detection and feature location of driver's face on highway, predicting face and feature location via a coarse-to-fine pattern. The proposed algorithm is verified under various complex highway conditions. Experimental results show that the proposed model shows satisfied performance compared to other state-of-the-art techniques used in driver face detection and alignment, keeping robust to the occlusions, varying pose and extreme illumination on highway.

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An Effective Method for Forest Fire Smoke Detection

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Journal of Physics: Conference Series 2019年第5期1187卷

作者： Luxing Qin Xuehui Wu Yichao Cao Xiaobo Lu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

This paper focuses on smoke detection in forest environments. In this paper, dark channel prior and OTSU based multi-threshold are used to find the disturbances such as sky and haze. These regions are blocked to reduce false alarm rate. A motion detection method based on frame difference is adapted to find the motion objects. Color moments, HOG and LBP are chosen as features of smoke and SVM is used as the classification. To reduce more false alarms, the motion regions are classified for several consecutive frames. They won't be regarded as smoke regions unless M frames of them are classified as smoke ones. Experiment results showed that the proposed method can detect smoke in video effectively and work real-timely.

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Towards End-to-End Face Recognition Method through Spatial Transformer

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yanfang Wu Xiaobo Lu Chen Qin School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Face recognition technology is widely applied in daily life, but in most methods, similarity or affine transformation is employed to align face images according to five facial landmarks. The face alignment module is implemented independently, thus it's difficult in end-to-end training. In this paper, the main purpose is to design a towards end-to-end trainable face recognition method based on indoor scenes. Due to that spatial transformer can implement any parametrizable transformation, we joint it with recognition network, making end-to-end training possible. Simultaneously, any prior knowledge on facial landmarks isn't required. The model jointly with spatial transformer can achieve 0.3% higher accuracy than similarity transformation. Most downsampling methods ignore the sampling theorem, making convolutional networks not shift-invariant. We replace max-pooling by MaxBlurPool in spatial transformer network, and the accuracy is improved by 0.25%.

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Prescriptive Manufacturing in Society 5.0: Human Autonomous Organizations and “On-Demand” Smart Services

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IFAC-PapersOnLine 2024年第3期58卷 139-144页

作者： Xiao Wang Jun Huang Jing Yang Xiaoxing Wang Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei 230601 China Macau University of Science and Technology Macau 999078 China School of artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Beijing SANBODY Technology Company Ltd Beijing 214000 China Qingdao Academy of Intelligent Industries Qingdao 266109 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Society 5.0 addresses human behaviors and their consequences in social management, while emphasizing the integration of Cyber-Physical-Social Spaces to achieve a highly connected and circulated society. Prompted by the various social needs expressed on social media platforms, Prescriptive Manufacturing (PM) emerges as a proactive pre-manufacturing paradigm. This paper delves into PM within the context of Society 5.0. It highlights the development of Human Autonomous Organizations (HAOs) and the delivery of “on-demand” smart services, which are customized to meet both individual and broader societal demands. The social sensing framework of social needs across different social media platforms that enables the identification and anticipation of consumer demands in real-time is illustrated. This data-driven insight leverages artificial intelligence and robotics to facilitate customized design and on-demand production, aligning product creation with real user requirements. Furthermore, we discuss the implementation of human-centered smart services in PM, such as virtual try-on, virtual fitting, personalized retrofit, user-friendly scheduling, and proximity logistics, which not only enhance consumer experience but also optimize resource efficiency By examining the symbiosis between technological advancements and social need fulfillment, this study sheds light on how PM and HAOs can foster a more responsive, sustainable, and inclusive economy in society, ultimately contributing to a higher quality of life and sustainable development goals of Society 5.0.

关键词： Social Needs Sensing Human Autonomous Organization Customized Design personalized fine-tuning prescriptive manufacturing Human-Centric Smart Services

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Antagonism between ambient ozone increase and urbanization-oriented population migration on Chinese cardiopulmonary mortality

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The Innovation 2023年第6期4卷 48-58页

作者： Haitong Zhe Sun Junchao Zhao Xiang Liu Minghao Qiu Huizhong Shen Serge Guillas Chiara Giorio Zosia Staniaszek Pei Yu Michelle W.L.Wan Man Mei Chim Kim Robin van Daalen Yilin Li Zhenze Liu Mingtao Xia Shengxian Ke Haifan Zhao Haikun Wang Kebin He Huan Liu Yuming Guo Alexander T.Archibald lYusuf Hamied Department of Chemistry University of CambridgeCambridge CB21EWUK Department of Earth Sciences University of CambridgeCambridge CB23EQUK Department of Environmental Health and Engineering Johns Hopkins Bloomberg School of Public HealthBaltimoreMD 21205USA State Key Joint Laboratory of ESPC State Environmental Protection Key Laboratory of Sources and Control of Air Pollution ComplexSchool of EnvironmentTsinghua UniversityBejing 100084China School of Atmospheric Sciences Nanjing UniversityNanjing 210023China Department of Earth System Science Stanford UniversityStanfordCA 94305USA School of Environmental Science and Engineering Southern University of Science and TechnologyShenzhen 518p55China Department of Statistical Science University College LondonLondon WC1E 6BTUK The Alan Turing Institute London NW12DBUK School of Public Heath and Preventive Medicine Monash UniversityMelboumeVIC 3004Australia British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary CareUniversity of CambridgeCambridge CB18RNUK Heart and Lung Research Institute University of CambridgeCambridge CB20BDUK Baroelona Supercomputing Center Department of Earth Sciences08034 BarcelonaSpain School of Environmental Science and Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Mathematics University of CaliformiaLos AngelesLos AngelesCA 90095USA State Key Laboratory of New Ceramics and Fine Processing KeyLaboratory of Advanced Materials of Ministry of EducationSchool of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Department of Engineering University of CambridgeCambridge CB21PZUK National Centre for Atmospheric Science Cambridge CB21EWUK

Everincreasing ambient ozone(O3)pollution in China has been exacerbating cardiopulmonary premature ***,the urban-rural exposure inequity has seldom been ***,we assess populationcale 03 exposure and mortality burdens between 1990 and 2019 based on integrated pollution tracking and epidemiological *** find Chinese population have been suffering from climbing 03 exposure by 4.3±2.8 ppb per decade as a result of rapid urbanization and growing prosperity of socioeconomic *** residents are broadly exposed to 9.8±4.1 ppb higher ambient O3 than the adjacent urban citizens,and thus urbaniza-tion-oriented migration compromises the exposure-associated mortality on total *** excess premature deaths attributable to longterm 03 exposure,373,500(95%uncertainty interval[U]:240,600-510,900)in 2019,is underestimated in previous studies due to ignorance of cardiovascular *** 03 pollution policy should focus more on rural population who are facing an aggravating threat of mortality risks to ameliorate environmental health injustice.

关键词： ambient urban urbanization

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Cloud Classification of Ground-based Cloud Images based on Convolutional Neural Network

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Journal of Physics: Conference Series 2021年第1期2035卷

作者： Tingting Zhu Liang Wei Yiren Guo College of Mechanical and Electronic Engineering Nanjing Forestry University Nanjing 210037 China Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University) Ministry of Education Nanjing 210096 China

Cloud is a crucial meteorological factor, and also a key factor affecting solar radiation variables for solar energy, especially the attenuation of different cloud types on solar radiation. However, there are few researches on the cloud classification based on the ground-based cloud image in order to predict solar energy. In this paper, the ground-based cloud images were collected, and then they were classified into five classes based on colour, texture and the attenuation on the theoretical clear-sky solar radiation. The ResNet18 model was trained to classify the ground-based cloud images automatically. The experimental results show that the classification accuracy of the ResNet18 is up to 91%, which is higher than the other two convolutional neural networks (AlexNet and VGG16) and is also much higher than the traditional machine learning classifiers. This work provides a new perspective and important supporting data for solar radiation forecast.

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