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Survey and Prospect for Applying Knowledge Graph in Enterprise Risk Management

Computers, Materials & Continua 2024年第3期78卷 3825-3865页

作者： Pengjun Li Qixin Zhao Yingmin Liu Chao Zhong Jinlong Wang Zhihan Lyu School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Department of Game Design Uppsala UniversityVisby62167Sweden

Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market *** the challenges posed by intricate and unpredictable risk factors,knowledge graph technology is effectively driving risk management,leveraging its ability to associate and infer knowledge from diverse *** review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business ***,employing bibliometric methods,the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge *** the succeeding section,systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge *** comparing and summarizing the strengths and weaknesses of each method,we provide recommendations for addressing the existing challenges in the construction ***,categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards,and furnishing a detailed exposition on the applicability of technical routes and ***,the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed,and relevant improvement suggestions were *** and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.

关键词： Knowledge graph enterprise risk risk identification risk management review

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Application of Response Surface Methodology for Analysing and Optimizing the Finned Solar Air Heater

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Journal of Thermal Science 2024年第3期33卷 985-1009页

作者： Vineet SINGH Vinod Singh YADAV Vaibhav TRIVEDI Manoj KUMAR Niraj KUMAR Department of Mechanical Engineering School of Engineering&TechnologyIFTM UniversityMoradabadUttar PradeshIndia Department of Mechanical Engineering NITUttarakhandPauriUttarakhandIndia Department of Electronics and Communication Engineering Jaypee Institute of Information TechnologyNoidaUttar Pradesh201309India

In this research paper,a solar air heater with triangular fins has been experimentally analysed and ***,an experimental set-up of a solar air heater having triangular fins has been developed at the location of 28.10°N,78.23°*** heat transfer rate through fins and fins efficiency has been determined by the Finite Difference Method model *** experimental data and modeled data of response parameters have been optimized in MINITAB-17 software by the Response Surface Methodology *** creating the response surface design,three input parameters have been selected namely solar intensity,Reynolds number,and fin base-to-height *** range of solar intensity,Reynolds number,and fin base-to-height ratio is 600 to 1000W/m^(2),4000 to 6000,and 0.4 to 0.8 *** response surface design has been analyzed by calculating the outlet temperature,friction factor,Nusselt number,fin efficiency,thermal performance factor,and exergy *** optimum settings of input parameters:solar intensity is 1000 W/m^(2);Reynolds number is 4969.7,and the fin base to height ratio is 0.6060,on which these response:namely outlet temperature of 92.531℃,friction factor of 0.2350,Nusselt number of 127.761,thermal efficiency of 50.836%,thermal performance factor of 1.4947,and exergy efficiency of 8.762%.

关键词： solar air heater optimization fins exergy response surface methodology air

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Research on Performance Optimization of Spark Distributed Computing Platform

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Computers, Materials & Continua 2024年第5期79卷 2833-2850页

作者： Qinlu He Fan Zhang Genqing Bian Weiqi Zhang Zhen Li School of Information and Control Engineering Xi’an University of Architecture and TechnologyXi’an710054China Shaanxi Institute of Metrology Science Xi’an710043China

Spark,a distributed computing platform,has rapidly developed in the field of big *** in-memory computing feature reduces disk read overhead and shortens data processing time,making it have broad application prospects in large-scale computing applications such as machine learning and image ***,the performance of the Spark platform still needs to be *** a large number of tasks are processed simultaneously,Spark’s cache replacementmechanismcannot identify high-value data partitions,resulting inmemory resources not being fully utilized and affecting the performance of the Spark *** address the problem that Spark’s default cache replacement algorithm cannot accurately evaluate high-value data partitions,firstly the weight influence factors of data partitions are modeled and ***,based on this weighted model,a cache replacement algorithm based on dynamic weighted data value is proposed,which takes into account hit rate and data *** integration and usage strategies are implemented based on LRU(LeastRecentlyUsed).Theweight update algorithm updates the weight value when the data partition information changes,accurately measuring the importance of the partition in the current job;the cache removal algorithm clears partitions without useful values in the cache to releasememory resources;the weight replacement algorithm combines partition weights and partition information to replace RDD partitions when memory remaining space is ***,by setting up a Spark cluster environment,the algorithm proposed in this paper is experimentally *** have shown that this algorithmcan effectively improve cache hit rate,enhance the performance of the platform,and reduce job execution time by 7.61%compared to existing improved algorithms.

关键词： Spark memory optimization memory replacement strategy

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An Attack on a Proposed Construction of Small-State Stream Ciphers and Proposals for New Constructions (Nov, bxac165, 2022)

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COMPUTER JOURNAL 2024年第2期67卷 806-806页

作者： Amin-Ghafari, Vahid Attari, Mahmoud Ahmadian School of Information and Control Engineering China University of Mining and Technology Jiangsu 221116 P. R. China Electrical and Computer Engineering School K. N. Toosi University of Technology Tehran *** Iran

Small-state stream ciphers (SSCs) idea is based on using key bits not only in the initialization but also continuously in the keystream generation phase. A time-memory-data tradeoff (TMDTO) distinguishing attack was successfully applied against all SSCs in 2017 by Hamann et al. They suggested using not only key bits but also initial value (IV) bits continuously in the keystream generation phase to strengthen SSCs against TMDTO attacks. Then, Hamann and Krause proposed a construction based on using only IV bits continuously in the packet mode. They suggested an instantiation of an SSC and claimed that it is resistant to TMDTO attacks. We point out that accessing IV bits imposes an overhead on cryptosystems that might be unacceptable in some applications. More importantly, we show that the proposed SSC remains vulnerable to TMDTO attacks 1. To resolve this security threat, the current paper proposes constructions based on storing key or IV bits that are the first to provide full security against TMDTO attacks. Five constructions are proposed for different applications by considering efficiency. Designers can obtain each construction’s minimum volatile state length according to the desirable keystream, key and IV lengths.

关键词： cryptography distinguishing attack IoT security lightweight encryption stream cipher time-memory-data tradeoff attack

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Broad-Learning-System-Based Model-Free Adaptive Predictive control for Nonlinear MASs Under DoS Attacks

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 381-393页

作者： Hongxing Xiong Guangdeng Chen Hongru Ren Hongyi Li the School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong University of Technology Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangdong University of Technology the College of Electronic and Information Engineering Southwest University IEEE

In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.

关键词： Broad learning technique denial-of-service (DoS) model-free adaptive predictive control (MFAPC) nonlinear multi-agent systems (NMASs)

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Noise-Aware Intermediary Fusion Network for Off-Road Freespace Detection

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Lv, Ying Liu, Zhi Li, Gongyang Chang, Xiaojun Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Engineering and Information Technology University of Technology Sydney Australia

Freespace detection holds a crucial role in autonomous driving technology, particularly in unstructured offroad scenarios that present additional challenges compared to structured road environments. Multimodal fusion methods are widely recognized as effective strategies for addressing these challenges. However, current fusion methods often overlook the critical issue of noise interference in multimodal data. To address this issue, we propose a novel Noise-Aware Intermediary Fusion Network for off-road freespace detection, named NAIFNet. This framework is specifically designed to mitigate noise interference during multimodal fusion. The key component of NAIFNet is the Noise-Aware Intermediary Interaction (NAII) module, which incorporates a denoising template as an intermediary during the fusion process. The NAII module employs multimodal features as query vectors, while the key and value vectors are derived from the search region features of the denoising template. At the same time, noise-aware interaction ensures effective denoising for data in each modality. Furthermore, in the decoding phase, we introduce the Denoising-Guided Decoder (DGD). Leveraging the denoising template, this decoder achieves more precise feature restoration and effectively mitigates the impact of noise. Extensive experiments on the popular benchmark of the offroad freespace detection dataset (ORFD) demonstrate that the proposed NAIFNet achieves state-of-the-art performance. IEEE

关键词： Noise abatement

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Parallel Implementation of the CCSDS Turbo Decoder on GPU

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China communications 2024年第10期21卷 70-77页

作者： Liu Zhanxian Liu Rongke Zhang Haijun Wang Ning Sun Lei Wang Jianquan School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China

This paper presents a software turbo decoder on graphics processing units(GPU).Unlike previous works,the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space Data Systems(CCSDS)***,the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism *** mitigate this issue,we propose a padding method that inserts several bits before the information frame *** obtain low-latency performance and high resource utilization,two-level intra-frame parallelisms and an efficient data structure are *** presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution(LTE)turbo codes with only small *** proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50Mbps throughputs for the code rates 1/6 and 1/2,respectively.

关键词： CCSDS CUDA GPU parallel decoding turbo codes

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Mask R-CNN based deep learning analysis on in-situ measured crystal images with automatic dataset labelling 41

Mask R-CNN based deep learning analysis on in-situ measured ...

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第41届中国控制会议

作者： Chen Jiang Tao Liu Timing Yang Zhenyu Jiang Honglin Liu School of Control Science and Engineering Dalian University of Technology School of Information and Communication Engineering Dalian University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

For real-time measurement of crystal size distribution(CSD) by in-situ captured crystal images,a deep-learning based image analysis method is proposed to improve measurement accuracy and efficiency,based on the well recognized maskregional convolutional neural network(Mask R-CNN).An automatic dataset labelling algorithm is established to facilitate preparing the training dataset that is required to include a large number of crystal image samples for effective deep ***,an image thresholding segmentation algorithm is introduced to extract the region of interest(ROI) in each crystal image sample for training the Mask R-CNN,such that improved segmentation accuracy and efficiency could be obtained for online image analysis to measure CSD during a crystallization *** results on measuring the crystallization process of β form L-glutamic acid(β-LGA) are shown to verify the effectiveness and advantage of the proposed method.

关键词： crystal image analysis in-situ measurement Mask R-CNN image labelling crystal size distribution L-glutamic acid crystallization process

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Efficiently Measure the Topologies of Large-Scale Networks Under the Guidance of Neural Network Gradients

IEEE Networking Letters

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IEEE Networking Letters 2023年第4期5卷 250-254页

作者： Fei, Gaolei Li, Zeyu Zhou, Yunpeng Zhai, Xuemeng Ye, Jian Hu, Guangmin University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

In this letter, we propose a novel network topology measurement method called the neural network gradient-guided method (NGM), which can use traceroutes to efficiently collect topological information (IPs and links) by selecting appropriate destination IPs. In this method, the mapping between probing destination IPs and probing revenues is modelled into a multiclassification problem using a neural network model, and networks are iteratively probed under the guidance of the neural network gradients. Experimental results in real networks demonstrated that NGM can collect more IPs and links by comparing with the methods that randomly selects IPs and subnets for probing. © 2019 IEEE.

关键词： Neural networks

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SIE: infrared and visible image fusion based on scene information embedding

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Multimedia Tools and Applications 2025年第3期84卷 1463-1488页

作者： Geng, Yingnan Diao, Weixuan Zhao, Yan Electrical and Information Engineering Changchun Institute of Technology Kuanping Road Jilin Changchun130103 China Information and Control Engineering Jilin Institute of Chemical Technology Chengde Street Jilin Jilin132022 China College of Communication Engineering Jilin University Qianjin Street Jilin Changchun130015 China

In this article, we proposed an infrared and visible image fusion method based on scene information embedding, which is to obtain an fused image with high-quality target information such as pedestrian crossing, vehicles, signs and rich background texture information. First, scene information is described, so as to effectively represent highlight salient target for infrared image and texture detail for visible image. Second, we embed this scene information into multi-scale fusion framework to fuse these information and reconstruct the desired fused image. It is worth noting that scene information is constructed by pre-trained convolutional neural network, where not only information extracted by convolutional neural network is fully utilized, but also training complexity is not increased. In datasets LLVIP, MSRS, M3FD,TNO, RoadScene and elctricity1, the superiority of our proposed algorithm SIE over state-of-the-arts is demonstrated by subjective evaluation and objective evaluation including information entropy (EN), standard deviation (SD), spatial frequency (SF), average gradient (AG), sum of the correlations of differences (SCD) and mutual information (MI) metrics. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image fusion

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