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Corrigendum to “Makerchain: A blockchain with chemical signature for self-organizing process in social manufacturing” [J. Clean. Prod. 234 (2019) 767–778]

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Journal of Cleaner Production 2022年 360卷

作者： Jiewu Leng Pingyu Jiang Kailin Xu Qiang Liu J. Leon Zhao Yiyang Bian Rui Shi Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment And School of Biomedical and Pharmaceutical Sciences Guangdong University of Technology Guangzhou Guangdong 510006 China Shenzhen Research Institute City University of Hong Kong Shenzhen 518057 China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an Shaanxi 710049 China School of Information Management Nanjing University Nanjing Jiangsu 210000 China

Ai driven heterogeneous MEC system for dynamic environment - Challenges and solutions

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource schEduling (ARE) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

A Localization Method of High Energy Transients for All-Sky Gamma-Ray Monitor

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arXiv 2022年

作者： Zhao, Yi Xue, Wangchen Xiong, Shaolin Luo, Qi Wang, Yuanhao Liu, Jiacong Yu, Heng Zhao, Xiaoyun Huang, Yue Liao, Jinyuan Sun, Jianchao Li, Xiaobo Yi, Qibin Cai, Ce Xiao, Shuo Xie, Shenglun Zheng, Chao Zhang, Yanqiu Wang, Chenwei Tan, Wenjun Guo, Zhiwei Li, Chaoyang An, Zhenghua Chen, Gang Du, Yanqi Gao, Min Gong, Ke Guo, Dongya He, Jiang He, Jianjian Li, Bing Li, Gang Li, Xinqiao Liang, Jing Liang, Xiaohua Liu, Yaqing Ma, Xiang Qiao, Rui Song, Liming Song, Xinying Sun, Xilei Wang, Jin Wang, Ping Wen, Xiangyang Wu, Hong Xu, Yanbing Yang, Sheng Zhang, Dali Zhang, Fan Zhang, Hongmei Zhang, Peng Zhang, Shu Zhang, Zhen Zheng, Shijie Zhang, Keke Han, Xingbo Wu, Haiyan Hu, Tai Geng, Hao Lu, Gaopeng Xu, Wei Lyu, Fanchao Zhang, Hongbo Lu, Fangjun Zhang, Shuangnan Department of Astronomy Beijing Normal University Beijing100875 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China School of Physics and Optoelectronics Xiangtan University Hunan Xiangtan411105 China College of Physics Hebei Normal University Shijiazhuang050024 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China Institute of Astrophysics Central China Normal University Wuhan430079 China College of physics Sciences Technology Hebei University Baoding071002 China Physics and Space Science College China West Normal University Nanchong637002 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China College of Electronic and information Engineering Tongji University Shanghai201804 China Innovation Academy for Microsatellites of Chinese Academy of Sciences Shanghai201304 China National Space Science Center Chinese Academy of Sciences Beijing100190 China School of Earth and Space Sciences University of Science and Technology of China Hefei230026 China Electronic Information School Wuhan University Hubei Wuhan430072 China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint China Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China

Fast and reliable localization of high-energy transients is crucial for characterizing the burst properties and guiding the follow-up observations. Localization based on the relative counts of different detectors has been widely used for all-sky gamma-ray monitors. There are two major methods for this counts distribution localization: χ2 minimization method and the Bayesian method. Here we propose a modified Bayesian method that could take advantage of both the accuracy of the Bayesian method and the simplicity of the χ2 method. With comprehensive simulations, we find that our Bayesian method with Poisson likelihood is generally more applicable for various bursts than χ2 method, especially for weak bursts. We further proposed a location-spectrum iteration approach based on the Bayesian inference, which could alleviate the problems caused by the spectral difference between the burst and location templates. Our method is very suitable for scenarios with limited computation resources or time-sensitive applications, such as in-flight localization software, and low-latency localization for rapid follow-up observations. Copyright © 2022, The Authors. All rights reserved.

关键词： Gamma rays

Boundary Guidance Hierarchical Network for Real-Time Tongue Segmentation

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arXiv 2020年

作者： Zeng, Xinyi Zhang, Qian Chen, Jia Zhang, Guixu Zhou, Aimin Wang, Yiqin Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai200062 China Department of Integrative Medicine on Pediatrics Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine Shanghai200127 China Shanghai Key Laboratory of Health Identification and Assessment Laboratory of Traditional Chinese Medicine Four Diagnostic Information Shanghai University of Traditional Chinese Medicine

—Automated tongue image segmentation in tongue images is a challenging task for two reasons: 1) there are many pathological details on the tongue surface, which affect the extraction of the boundary;2) the shapes of the tongues captured from various persons (with different diseases) are quite different. To deal with the challenge, a novel end-to-end Boundary Guidance Hierarchical Network (BGHNet) with a new hybrid loss is proposed in this paper. In the new approach, firstly Context Feature Encoder Module (CFEM) is built upon the bottom-up pathway to confront with the shrinkage of the receptive field. Secondly, a novel hierarchical recurrent feature fusion module (HRFFM) is adopt to progressively and hierarchically refine object maps to recover image details by integrating local context information. Finally, the proposed hybrid loss in a four hierarchy—pixel, patch, map and boundary guides the network to effectively segment the tongue regions and accurate tongue boundaries. BGHNet is applied to a set of tongue images. The experimental results suggest that the proposed approach can achieve the latest tongue segmentation performance. And in the meantime, the lightweight network contains only 15.45M parameters and performs only 11.22GFLOPS. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

A Novel Method of Detecting Microangioma in Retinal Image Based on Visual Characteristics

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A Novel Method of Detecting Microangioma in Retinal Image Ba...

IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Xinrong Cao Hongkai Lin Hongliang Kang College of Computer and Control Engineering Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

Diabetic retinopathy (DR), an ocular complication of diabetes could threaten vision, has become one of the common causes of blindness in adults' working age. The detection of microangioma (MA), the first feature and persists with the development of DR, is an important step in the diagnosis and follow-up of DR. However, due to complexity of retinal image and the randomness of abnormal lesions, the detection of MA is more susceptible to the influence of background texture noise and other structures in retinal image. A novel method for detecting MA based on visual features is proposed. It establishes an observation function and uses multi-range mean filtering to eliminate texture noise and vascular network. Then, the detection target is confirmed by the visual shape features. A series of tests in the different public retinal images databases show that the proposed method is effective and would be useful for computer-aided diagnosis of early screening of DR.

关键词： Retina Feature extraction Shape Biomedical imaging Blood vessels Visualization Filtering

Synaptic learning with augmented spikes

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arXiv 2020年

作者： Yu, Qiang Song, Shiming Ma, Chenxiang Pan, Linqiang Tan, Kay Chen Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Image Information Processing and Intelligent Control Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China The Department of Computer Science City University of Hong Kong Hong Kong

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements on efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this paper, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insight into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness and applicability to practical tasks such as acoustic and visual pattern recognition. The remarkable results highlight the effectiveness and potential merits of our methods. Importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them including neuromorphic computing. Copyright © 2020, The Authors. All rights reserved.

关键词： Neurons

Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification

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arXiv 2020年

作者： Qu, Ying Baghbaderani, Razieh Kaviani Li, Wei Gao, Lianru Zhang, Yuxiang Qi, Hairong The Advanced Imaging and Collaborative Information Processing Group Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Hyperspectral image (HSI) classification is one of the most active research topics and has achieved promising results boosted by the recent development of deep learning. However, most state-of-the-art approaches tend to perform poorly when the training and testing images are on different domains, e.g., source domain and target domain, respectively, due to the spectral variability caused by different acquisition conditions. Transfer learning-based methods address this problem by pre-training in the source domain and fine-tuning on the target domain. Nonetheless, a considerable amount of data on the target domain has to be labeled and non-negligible computational resources are required to retrain the whole network. In this paper, we propose a new transfer learning scheme to bridge the gap between the source and target domains by projecting the HSI data from the source and target domains into a shared abundance space based on their own physical characteristics. In this way, the domain discrepancy would be largely reduced such that the model trained on the source domain could be applied on the target domain without extra efforts for data labeling or network retraining. The proposed method is referred to as physically-constrained transfer learning through shared abundance space (PCTL-SAS). Extensive experimental results demonstrate the superiority of the proposed method as compared to the state-of-the-art. The success of this endeavor would largely facilitate the deployment of HSI classification for real-world sensing scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Application of Image processing technology in Building Surface Crack Detection 6

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Application of Image Processing Technology in Building Surfa...

2019 6th International Conference on Advanced Composite Materials and Manufacturing Engineering, ACMME 2019

作者： Xiong, Xin Huang, Li Tian, Jinrong Infrastructure and Logistics Management Wuhan University of Science and Technology Wuhan Hubei430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan Hubei430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan Hubei430081 China

The phenomenon of cracks on the surface of buildings is widespread. The existence of cracks affects the normal use of buildings, shortening the service life, seriously damaging the structure of buildings, resulting in safety accidents. Therefore, regular detection and reinforcement of cracks on the surface of buildings is a necessary link to ensure safety. At present, the detection of building surface cracks is mainly carried out by manpower with means of equipment assisted. However, there are some problems in manpower detection, such as high manpower consumption, low *** paper presents a method of detecting and inspecting cracks on building surface based on image processing technology in machine vision, which includes image gray transformation, image denoising etc. Curve fitting and least square method are used to obtain crack parameters, so as to improve the inspecting efficiency and realize the automation of building surface inspection. © Published under licence by IOP Publishing Ltd.

关键词： Curve fitting

Modified Image Haze Removal Algorithm Based on Dark Channel Prior

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Modified Image Haze Removal Algorithm Based on Dark Channel ...

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Junpeng Hu Zuoyong Li Xinwei Chen Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Image defogging/dehazing is an important research hotspot in the field of image processing. The purpose of image defogging is to remove fog and obtain more image details. However, image defogging results based on dark channel prior usually have the problems of color distortion and insufficient brightness, which leads to the loss of partial image information. In order to solve this problem, we proposed a new image defogging algorithm based on dark channel prior. The proposed algorithm first selects the candidate regions of the sky by combining the location priori and brightness characteristics of the image pixel points, and reasonably estimate the atmospheric light value via the pixel points in the candidate regions. Second, the proposed algorithm respectively reduces the RGB (Red, Green, and Blue) channel values of the fog image, and combines each reduced channel and the other two previously unreduced channels to make up three new fog images. Finally, the proposed algorithm uses our image defogging algorithm to get three defogging images, and weights three defogging images to get the restored image. Comparative study and qualitative evaluation indicated that the proposed algorithm can obtain clearer defogging image.

关键词： Image color analysis Brightness Channel estimation Distortion Image restoration Estimation Atmospheric modeling