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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Xiaoyan Hu Xiaoyi Song Guang Cheng Jian Gong Lu Yang Honggang Chen Zhichao Liang School of Cyber Science & Engineering Southeast University Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration (Southeast University) China Research Base of International Cyberspace Governance (Southeast University) Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Jiangsu Province Engineering Research Center of Security for Ubiquitous Network Nanjing China JiangSu Peerfintech Technology Co. Ltd.

ISBN: (纸本)9781665406697

Blockchain is well known for its storage consistency, decentralization and tamper-proof, but the privacy disclosure and difficulty in auditing discourage the innovative application of blockchain technology. As compared to public blockchain and private blockchain, consortium blockchain is widely used across different industries and use cases due to its privacy-preserving ability, auditability and high transaction rate. However, the present co-audit of privacy-preserving data on consortium blockchain is inefficient. Private data is usually encrypted by a session key before being published on a consortium blockchain for privacy preservation. The session key is shared with transaction parties and auditors for their access. For decentralizing auditorial power, multiple auditors on the consortium blockchain jointly undertake the responsibility of auditing. The distribution of the session key to an auditor requires individually encrypting the session key with the public key of the auditor. The transaction initiator needs to be online when each auditor asks for the session key, and one encryption of the session key for each auditor consumes resources. This work proposes GAChain and applies group key agreement technology to efficiently co-audit privacy-preserving data on consortium blockchain. Multiple auditors on the consortium blockchain form a group and utilize the blockchain to generate a shared group encryption key and their respective group decryption keys. The session key is encrypted only once by the group encryption key and stored on the consortium blockchain together with the encrypted private data. Auditors then obtain the encrypted session key from the chain and decrypt it with their respective group decryption key for co-auditing. The group key generation is involved only when the group forms or group membership changes, which happens very infrequently on the consortium blockchain. We implement the prototype of GAChain based on Hyperledger Fabric framework. Our

关键词： Industries Privacy Data privacy Distributed ledger Public key Prototypes Fabrics

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A NOMA Power Allocation Method Based on Greedy Algorithm 8th

A NOMA Power Allocation Method Based on Greedy Algorithm

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8th International Conference on Communications, Signal processing, and Systems, CSPS 2019

作者： Lu, Yin Chen, Shuai Mao, Kai Bian, Haowei Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Nanjing University of Posts and Telecommunications Ministry of Education Nanjing210003 China Nanjing University of Posts and Telecommunications Nanjing210003 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789811394089

In the existing Non-Orthogonal Multiple Access power allocation algorithm, the iterative water-filling algorithm is a commonly used algorithm, which has good performance but high complexity. In order to reduce the complexity, this paper divides the power allocation problem of Non-Orthogonal Multiple Access into two steps. Firstly, the water-filling algorithm is used to complete the power allocation between sub-carriers, then the greedy algorithm is used to allocate power to the superimposed users in the carrier. Many elements in the candidate power allocation coefficient set that are impossible to be optimal solutions are deleted by using the sum of power distribution coefficients of each user and the product of throughput, which effectively reducing the complexity. The simulation results show that the proposed algorithm has a slightly lower performance than the iterative water injection algorithm, but it effectively reduces the complexity. The performance of the proposed algorithm is better than other traditional algorithms, and a good compromise between system performance and complexity is achieved. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Filling

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Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

arXiv

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arXiv 2020年

作者： Hang, R. Li, Z. Ghamisi, P. Hong, D. Xia, G. Liu, Q. Jiangsu Key Laboratory of Big Data Analysis Technology School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer Science and Electrical Engineering University of Missouri Kansas CityMO64110 United States Exploration FreibergD-09599 Germany Wessling82234 Germany Munich80333 Germany Jiangsu Key Laboratory of Big Data Analysis Technology School of Automation Nanjing University of Information Science and Technology Nanjing210044 China

This paper has been accepted by IEEE Transactions on Geoscience and Remote Sensing. In this paper, we propose an efficient and effective framework to fuse hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral-spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data. Both of them consist of three convolutional layers, and the last two convolutional layers are coupled together via a parameter sharing strategy. In the fusion phase, feature-level and decision-level fusion methods are simultaneously used to integrate these heterogeneous features sufficiently. For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy. For the decision-level fusion, a weighted summation strategy is adopted, where the weights are determined by the classification accuracy of each output. The proposed model is evaluated on an urban data set acquired over Houston, USA, and a rural one captured over Trento, Italy. On the Houston data, our model can achieve a new record overall accuracy of 96.03%. On the Trento data, it achieves an overall accuracy of 99.12%. These results sufficiently certify the effectiveness of our proposed model. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical radar

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The orbital period of the long-period and colliding-wind binary WR 146 from radio interferometry of the shock cone

arXiv

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arXiv 2025年

作者： Wen, Shiming Zhang, Bo Xu, Shuangjing Sun, Yan Mai, Xiaofeng Zhang, Jingdong Cui, Lang Li, Xiaofeng Todt, Helge Yan, Xi Jiang, Pengfei Shanghai Astronomical Observatory Chinese Academy of Sciences 80 Nandan Rd. Xuhui Shanghai200030 China Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science 1-Street Urumqi Xinjiang830011 China Yuquan Rd. Shijingshan Beijing100049 China Korea Astronomy and Space Science Institute 776 Daedeok-daero Yuseong-gu Daejeon34055 Korea Republic of A20 Datun Road Chaoyang District Beijing100101 China Xinjiang Key Laboratory of Radio Astrophysics 150 Science 1-Street Xinjiang Urumqi830011 China School of Computer Science and Information Engineering Changzhou Institute of Technology Jiangsu Changzhou213032 China Institut für Physik und Astronomie Universität Potsdam Karl-Liebknecht-Str. 24/25 PotsdamD-14476 Germany

We report the first measurement of the orbital period of a long-period colliding-wind binary (CWB) system WR 146, derived by tracing the rotational morphology of its wind-colliding region (WCR) and the relative orientation of the two binary components. This result is based on our imaging observations using the Very Long Baseline Array (VLBA) and the European Very Long Baseline Interferometry (VLBI) Network (EVN), combined with archival data from VLBA, EVN, the Very Large Array (VLA), the enhanced Multi-Element Radio-Linked Interferometer Network (eMERLIN) arrays, and optical images from the Hubble Space Telescope (HST). We evaluated two methods for determining the binary’s orbital period based on the images of the WCR: (I) fitting the shock cone of the WCR and (II) stacking images using the cross-correlation function. Using these techniques, we find orbital period estimates of 810+120−90 years from method I and 1120+540−270 years from method II, both of which support a long orbital period of approximately 1,000 years. Furthermore, we analyzed archival spectral data of WR 146 to estimate the stellar wind velocities of the binary components, finding no significant orbital phase lag between the binary orientation and the WCR rotation. We also estimate the range of the binary’s mass using the currently measured parameters. © 2025, CC BY-NC-SA.

关键词： Space telescopes

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An Integration Testing Framework and Evaluation Metric for Vulnerability Mining Methods

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China Communications 2018年第2期15卷 190-208页

作者： Jin Li Jinfu Chen Minhuan Huang Minmin Zhou Wanggen Xie Zhifeng Zeng Shujie Chen Zufa Zhang National key laboratory of science and technology on information system security Beijing institute of system engineering School of Computer Science and Communication Engineering Jiangsu University

Software an important way to vulnerability mining is detect whether there are some loopholes existing in the software, and also is an important way to ensure the secu- rity of information systems. With the rapid development of information technology and software industry, most of the software has not been rigorously tested before being put in use, so that the hidden vulnerabilities in software will be exploited by the attackers. Therefore, it is of great significance for us to actively de- tect the software vulnerabilities in the security maintenance of information systems. In this paper, we firstly studied some of the common- ly used vulnerability detection methods and detection tools, and analyzed the advantages and disadvantages of each method in different scenarios. Secondly, we designed a set of eval- uation criteria for different mining methods in the loopholes evaluation. Thirdly, we also proposed and designed an integration testing framework, on which we can test the typical static analysis methods and dynamic mining methods as well as make the comparison, so that we can obtain an intuitive comparative analysis for the experimental results. Final- ly, we reported the experimental analysis to verify the feasibility and effectiveness of the proposed evaluation method and the testingframework, with the results showing that the final test results will serve as a form of guid- ance to aid the selection of the most appropri- ate and effective method or tools in vulnera- bility detection activity.

关键词： software vulnerability detection loopholes information security evaluationmethod testing framework

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Multi-task learning for object keypoints detection and classification

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Pattern Recognition Letters 2020年 130卷 182-188页

作者： Jie Xu Lin Zhao Shanshan Zhang Chen Gong Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China State Key Laboratory of Integrated Services Networks Xidian Univeristy Xi’an 710071 China

Object keypoints detection and classification are both central research topics in computer vision . Due to their wide range potential applications in the real world, substantial efforts have been taken to advance their performance. However, these two related tasks are mainly treated separately in previous works. We argue that keypoints detection and classification can be complementary tasks and beneficial to each other. Knowing the category of a object is able to reduce the searching space of keypoints detection models and facilitate more precise localization . On the other hand, having the knowledge of object keypoints can make classification models pay more attention on areas that are more associated with the object, which will inevitably promote classification accuracy . Embracing this observation, we propose to model keypoints detection and classification in a multi-task learning framework. Specifically, a multi-task deep network is designed and trained to conduct both tasks, where we devise the model structure delicately to carry out sufficient training of both tasks. Extensive experiments are set up on the AIFASHION DATASET and Human3.6M DATASET to validate our proposal, we show that our algorithm outperforms separate models trained individually on each task.

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Exploiting dynamic nonlinearity in upconversion nanoparticles for super-resolution imaging

arXiv

引用

arXiv 2022年

作者： Chen, Chaohao Ding, Lei Liu, Baolei Du, Ziqin Liu, Yongtao Di, Xiangjun Shan, Xuchen Lin, Chenxiao Zhang, Min Xu, Xiaoxue Zhong, Xiaolan Wang, Jianfeng Chang, Lingqian Halkon, Ben J. Chen, Xin Cheng, Faliang Wang, Fan Guangdong Engineering and Technology Research Center for Advanced Nanomaterials School of Environment and Civil Engineering Dongguan University of Technology Dongguan523808 China Department of Chemical Engineering Shaanxi Key Laboratory of Energy Chemical Process Intensification Institute of Polymer Science in Chemical Engineering School of Chemical Engineering and Technology Xi'an Jiaotong University Xi'An710049 China School of Mathematical and Physical Sciences Faculty of Science University of Technology SydneyNSW2007 Australia School of Physics Beihang University Beijing100191 China Smart Computational Imaging Laboratory Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China Department for Electrochemical Energy Storage Helmholtz-Zentrum Berlin für Materialien und Energie Hahn-Meitner-Platz Berlin14109 Germany School of Biomedical Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing100191 China Centre for Audio Acoustics & Vibration Faculty of Engineering & IT University of Technology Sydney UltimoNSW2007 Australia School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

Single-beam super-resolution microscopy, also known as superlinear microscopy, exploits the nonlinear response of fluorescent probes in confocal microscopy. The technique requires no complex purpose-built system, light field modulation, or beam shaping. Here, we present a strategy to enhance this technique's spatial resolution by modulating excitation intensity during image acquisition. This modulation induces dynamic optical nonlinearity in upconversion nanoparticles (UCNPs), resulting in variations of higher spatial-frequency information in the obtained images. The high-order information can be extracted with a proposed weighted finite difference imaging algorithm from raw fluorescence images, to generate an image with a higher resolution than superlinear microscopy images. We apply this approach to resolve two adjacent nanoparticles within a diffraction-limited area, improving the resolution to 130 nm. This work suggests a new scope for developing dynamic nonlinear fluorescent probes in super-resolution nanoscopy. © 2022, CC BY.

关键词： Nanoparticles

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An improved approach for automatic spine canal segmentation using probabilistic boosting tree (PBT) with fuzzy support vector machine

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Journal of Ambient Intelligence and Humanized Computing 2020年第6期12卷 6527-6536页

作者： Viji, C. Rajkumar, N. Suganthi, S. T. Venkatachalam, K. kumar, T. Rajesh Pandiyan, Sanjeevi Department of CSE Akshaya College of Engineering and Technology Coimbatore India Department of Computer Engineering Lebanese French University Erbil Iraq School of CSE VIT University Bhopal Bhopal India Department of Information Technology Sri Krishna College of Technology Kovaipudur India Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China

Spine canal segmentation is an emerging zone in research proposed to help interpretation and processing of advanced MRI and CT images. For instance, high resolution three-dimensional volumes can be divided to provide an estimation of spine canal atrophy. Spine canal segmentation is complex because of assortment of MRI contrasts and variation in human life structures. This investigation illustrates the details of spine canal segmentation techniques and gives a few measurements that can be utilized to contrast with other segmentation strategies. The details of background and foreground subtraction techniques, spine canal segmentation approach and optimization approach which are utilized in the different applications have been considered. In this paper, spine canal segmentation on probabilistic booting tree (PBT) with fuzzy support vector machine performance measures and metrics are analysed in state-of-the art technologies. Proposed approach is performed on the base of the automatic spine canal segmentation with the group of data MR. This proposed segmentation continue with fuzzy support vector machine (FSVM) technique to make fully automatic stream pipeline. The declaration in an automatic segmentation of stream pipeline was implemented with flexible voxel wise classification accompanying dimensions analogous with 3D Haar and labelled machine learning algorithms i.e. probabilistic boosting tree combined fuzzy support vector machine (PBT-FSVM). The novel segmentation technique correlated with MR data sets provides better accuracy than the exiting techniques and it is shown in experimental outcomes. To still improve performance of the results, online learning classification method can be in the proposed work.

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

引用

ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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Mechanical Frequency Spectrum Sub-band Feature Estimation Based on Multiple Types Correlation Analysis

Mechanical Frequency Spectrum Sub-band Feature Estimation Ba...

引用

Chinese Control Conference (CCC)

作者： Jian Tang Meiying Jia Fupeng Wang Jian Zhang Gang Yu Faculty of Information Technology Beijing University of Technology Beijing China Joint staff of the Central Military Commission 55th Institute Beijing China 91411 Unit PLA Dalian China College of computer and software Nanjing University of Information Science and Technology Nanjing China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Mill load is one of the key factors that affects the operation optimization and control of grinding process. It is difficult to detect accurately in real time. The mill load relates to multiple channel mechanical signals, such as the mill shell vibration signals, the shaft seat front and rear vibration signals, the acoustic signals near to the shell surface and under the grinding area, etc. How to evaluate these sub-band feature of the mechanical spectrum of the above channels is a difficult problem. It can help to clarify the ball mill grinding mechanism and the mechanical signal generation mechanism. To solve these problems, this paper proposes a multi-channel mechanical frequency spectrum sub-band feature evaluation method based on multiple types correlation analysis. Firstly, the multi-channel mechanical signals are transformed from time domain to frequency domain to obtain the frequency spectrum data, which are divided into several sub-bands to extract features. Then, the normalized correlation coefficients between the multi-channel sub-band feature and the mill load are calculated. Thirdly, based on the partial least square algorithm, the variable projection importance (VIP) value of sub-band features are also calculated. The VIPs are normalized same as that of correlation coefficient. Finally, a new index is calculated by using normalized correlation coefficient and VIP value and the model's prediction performance. These sub-band features of single channel and all channel mechanical signals are measured based on the new defined combined evaluation index. The multi-channel mechanical signals of a laboratory-scale ball mill are used to verify the effectiveness of the proposed method.

关键词： Vibrations Feature extraction Correlation coefficient Shafts Acoustics Electronic mail Frequency-domain analysis

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