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9th IEEE International Conference on data Science in Cyberspace, DSC 2024

作者： Yu, Yonghao Guo, Yongde Zhao, Dawei Fu, Kexue Tang, Yongwei Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong University School of Mechanical Engineering Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Jinan China

ISBN: (纸本)9798350391367

Existing indoor visible light positioning methods, such as those based on fingerprint database algorithms, are complex to construct and consume significant resources. In response to this issue, the text proposes an end-to-end indoor visible light positioning method that samples continuous signals at a small number of random coordinate positions in indoor environments. Through a deep learning method based on attention mechanism, a nonlinear mapping between the received signal and spatial position coordinates is constructed, and the time series properties in the received signal are encoded as a feature in the data. The method is further improved by combining residual networks to improve accuracy. Through simulation experiments, it has been verified that the deep learning based localization method described in this article can achieve sub meter level accuracy on small sample datasets, and it has been proven that time series features have a significant impact on localization accuracy in indoor visible light positioning algorithms based on deep learning. © 2024 IEEE.

关键词： Time series

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Binocular active stereo matching based on multi-scale random forest 13

Binocular active stereo matching based on multi-scale random...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Liu, Jiatong Pu, Mingqi Wang, Ghutian Chen, Dongyue Jia, Tang College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University China Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China

ISBN: (纸本)9798350315196

With the rapid development of 3D vision technology, the existing passive binocular cameras can no longer meet the practical needs of depth perception. Therefore, this paper proposes a binocular active stereo matching method based on multi-scale random forest. Firstly, a binocular active vision system consists of a near-infrared random speckle projector and a binocular camera is constructed, and the system was calibrated using Zhang's calibration method. Secondly, gamma image enhancement and image difference method are used for processing to reduce the impact of ambient light on the measurement results. Next, extract the points of interest and use the multi-scale random forest algorithm to match the window where the points of interest are located to generate a sparse structured light anchor disparity map globally. Finally, using the Census transforms as the matching cost, iterate continuously, refine the disparity and get a dense disparity map. The experimental results show that this system can achieve good depth perception accuracy and robustness under complex indoor lighting conditions. © 2023 IEEE.

关键词： Binoculars

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PID-YOLOX: An X-Ray Prohibited Items Detector Based on YOLOX 13

PID-YOLOX: An X-Ray Prohibited Items Detector Based on YOLOX

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Li, Mingyuan Ma, Bowen Wang, Hao Li, Yingbo Chen, Dongyue Jia, Tong College of Information Science and Engineering Northeastern-University Shenyang China Foshan Graduate School of Innovation Northeastern University China Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China

ISBN: (纸本)9798350315196

X-ray prohibited items detection is an effective and crucial measure in various security inspection scenarios. However, the overlapping phenomenon in X-ray images exacerbates the foreground-background class imbalance, and the imaging principle of X-ray images results in missing texture features. To address these challenges, we propose an end-to-end X-ray Prohibited Items Detector (PID-YOLOX) based on YOLOX-Tiny, which offers fast detection speed and high accuracy. Specifically, we introduce the Generalized Label Assignment (GLA) scheme to tackle the foreground-background class imbalance problem, and the Multi-Cardinality Attention (MCA) mechanism to alleviate the issue of missing texture features. Our experimental results show that PID-YOLOX achieves 54.9% average precision (AP) on the PIXray dataset, surpassing YOLOX-Tiny by 2.2% AP. Furthermore, extensive experiments demonstrate that PID-YOLOX is superior to the state-of-the-art methods, indicating its potential applications in the prohibited items detection field. © 2023 IEEE.

关键词： Textures

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Single-Stage Grasping Algorithm for the Manipulator Based on Rotational Gaussian Encoding 13

Single-Stage Grasping Algorithm for the Manipulator Based on...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Liu, Zhenghao Liu, Yizhe Yang, Guowei Chen, Dongyue Jia, Tong College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University China Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China

ISBN: (纸本)9798350315196

As we all know, there is an unsolved problem for robots to accurately and quickly grasp unknown objects in an unstructured environment. In order to describe the pose information of objects more accurately, a rotational Gaussian encoding method is proposed in this paper. Different from the traditional 2-D Gaussian encoding to represent ground-truth, this method introduces rotation information based on 2-D Gaussian. Grasping is made more accurate and robust by treating the grasp pose as a rotating bounding box in the image plane. In addition, a balanced loss function is introduced in this paper. This loss function not only solves the problem of positive and negative sample imbalance in the traditional cross-entropy loss but also reduces the penalty of points around the Gaussian center location. In this paper, a pixel-level single-stage grasping algorithm is designed in an end-to-end manner without the intermediate process of using anchor points, which saves time and improves the accuracy of grasping simultaneously. Then, the proposed model in this paper is evaluated on two standard datasets, Cornell dataset and Jacquard dataset, achieving 96.8% and 94.7% accuracy, respectively. Finally, we use a 6 DoF UR5e robotic arm for real-world grasping experiments. The success rate of single-object grasping is 94.7% and the time to generate the grasp frame is 8 ms. Experiments demonstrat that the algorithm is equally effective in real-world environments and has a faster detection time. © 2023 IEEE.

关键词： Signal encoding

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Declined trends of chlorophyll a in the South China Sea over 2005–2019 from remote sensing reconstruction

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Acta Oceanologica Sinica 2023年第1期42卷 12-24页

作者： Tianhao Wang Yu Sun Hua Su Wenfang Lu School of Marine Sciences Sun Yat-Sen UniversityZhuhai519000China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai519000China College of Ocean and Earth Sciences Xiamen UniversityXiamen361102China State Key Laboratory of Marine Environmental Science Xiamen UniversityXiamen361102China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education Fuzhou UniversityFuzhou350108China National&Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou UniversityFuzhou350108China

Chlorophyll a concentration(CHL)is an important proxy of the marine ecological environment and phytoplankton ***-term trends in CHL of the South China Sea(SCS)reflect the changes in the ecosystem’s productivity and functionality in the regional carbon *** this study,we applied a previously reconstructed 15-a(2005–2019)CHL product,which has a complete coverage at 4 km and daily resolutions,to analyze the long-term trends of CHL in the *** regression was used to elaborate on the long-term trends of high,median,and low CHL values,as an extended method of conventional linear *** results showed downward trends of the SCS CHL for the 75th,50th,and 25th quantile in the past 15 a,which were−0.0040 mg/(m^(3)·a)(−1.62%per year),−0.0023 mg/(m^(3)·a)(−1.10%per year),and−0.0019 mg/(m^(3)·a)(−1.01%per year).The negative trends in winter(November to March)were more prominent than those in summer(May to September).In terms of spatial distribution,the downward trend was more significant in regions with higher *** led to a reduced standard deviation of CHL over time and *** further explored the influence of various dynamic factors on CHL trends for the entire SCS and two typical systems(winter Luzon Strait(LZ)and summer Vietnam Upwelling System(SV))with single-variate linear regression and multivariate Random Forest *** multivariate analysis suggested the CHL trend pattern can be best explained by the trends of wind speed and mixed-layer *** divergent importance of controlling factors for LZ and SV can explain the different CHL trends for the two *** study expanded our understanding of the long-term changes of CHL in the SCS and provided a reference for investigating changes in the marine ecosystem.

关键词： chlorophyll a concentration quantile trends remote sensing reconstruction South China Sea

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Improving Visual Reinforcement Learning via Spatial-Temporal data Augmentation

Improving Visual Reinforcement Learning via Spatial-Temporal...

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Jiabao Li Qingwen Hou Jie Zhang Xianzhong Chen Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Sample efficiency has been a challenging problem in visual reinforcement learning, where agents not only learn polices but also extract meaningful state representations for decision making from images. Reinforcement Learning methods that adopt data augmentation have significantly improved sample efficiency. However, prior studies have primarily focused on spatial augmentation, which enhances data diversity but insufficiently exploits temporal information, such as historical states which is critical for time-series-dependent reinforcement learning tasks. To address this gap, we introduce a Spatial-Temporal data Augmentation (STDA) framework designed to enhance the agent's ability to learn task-relevant state representations by effectively leveraging historical state information. We evaluate our approach using the DeepMind Control Suite, and empirical results demonstrate that STDA significantly improves performance, surpassing state-of-the-art methods across a range of tasks.

关键词： Visualization Q-learning Law enforcement Decision making Training data Reinforcement learning Benchmark testing data augmentation Consumer electronics

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Self-assembly of a new 3D platelike ternary-oxo-cluster:An efficient catalyst for the synthesis of pyrazoles

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Chinese Chemical Letters 2022年第1期33卷 354-357页

作者： Guoping Yang Yufeng Liu Xiaoling Lin Bangming Ming Ke Li Changwen Hu Jiangxi Province Key Laboratory of Synthetic Chemistry Jiangxi Key Laboratory for Mass Spectrometry and InstrumentationEast China University of TechnologyNanchang 330013China Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of TechnologyNanchang 330013China Key Laboratory of Cluster Science of Ministry of Education School of Chemistry and Chemical EngineeringBeijing Institute of TechnologyBeijing 100081China

A new metal-oxo-clusters-based inorganic framework[NaCo_(2)Mo_(2)O_(7)(OH)_(3)]_(n)(NaCoMo),named as 3D platelike ternary-oxo-cluster,has been hydrothermally synthesized and characterized by single-crystal X-ray diffraction structure analysis,FT-IR spectroscopy,powder X-ray diffraction(PXRD),scanning electron microscope(SEM),energy-dispersive X-ray spectroscopy(EDS)analyses,X-ray photoelectron spectroscopy(XPS)and thermogravimetric analysis(TGA).Structure analysis reveals that there are no classical building units in NaCoMo,and the asymmetric units of NaCoMo are directly extended into a new platelike 3D *** functional theory calculations(DFT)indicates that the crystal formation process is exothermic and the structure is extremely *** addition,the compound presents excellent catalytic activity in the condensation and cyclization reaction of sulfonyl hydrazides and 1,3-diketones to synthesize pyrazoles,and the yield of the desired product is up to 99%.The successful synthesis of NaCoMo represents the discovery of a new kind of non-classical polyoxometalates.

关键词： Polyoxometalates Oxo clusters 3D inorganic framework Condensation reaction Pyrazoles

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A knowledge Graph Completion Model with Path-Specific Learning

A Knowledge Graph Completion Model with Path-Specific Learni...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xiulin Zheng Zan Zhang Rui Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) and also with the School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Inspur Academy of Science and Technology Shandong China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

knowledge graphs (KGs) play an increasingly im-portant role in many knowledge-aware tasks. However, existing KGs are struggle with incompleteness, which motivates knowledge graph completion (KGC), that is, predicting the lost links between entities based on observed triples. Reasoning over relation paths in incomplete KGs is popular. Nonetheless, some significant issues are still remained to be addressed, such as path noise and ambiguity of inferred relation. To address these problems, we propose a novel path augmented _Reasoning model with avoidance of Path noise and Disambiguation of inferred relation in this paper, referred to as RPD. In this model, we calculate the sum of resource allocation for each relation path to measure its reliability to avoid the inference of path noise. To address the ambiguity of an inferred relation, we introduce position embedding to denote the relation position along the path when learning path representation. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of our proposal RPD model in the handling of KGC tasks compared to SOTAs.

关键词： Heavily-tailed distribution Noise knowledge graphs Benchmark testing Transformers Cognition Reliability Resource management Proposals Noise measurement

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Fault Assessment for Mechanical Equipment with Adaptive Weights Incorporating Sensitivity and Monotonicity

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IFAC-PapersOnLine 2023年第2期56卷 11796-11801页

作者： Yinghao Zhao Xu Yang Jian Huang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing PR China

Driven by the increasing needs in industrial processes for condition-based maintenance, this paper present an adaptive weighting strategy based on sensitivity and monotonicity for fault assessment. Since the sensitivities of features vary with the changes of fault severity, adaptive weight coefficients are designed based on sensitivities to strengthen the feature information. Meanwhile, considering the irreversibility of fault evolution and the difference of sensitive features to different faults, unique monotonic multi-domain feature set with high sensitivity can be selected. Finally, a monotonic health index (HI) is fused based on adaptive weight coefficients for fault assessment which satisfies the needs for intuitiveness in industrial sites. Moreover, the effectiveness of the proposed method is demonstrated by rolling bearing test rig. Results show that the average assessment accuracy can reach 87.5%, 95.375% and 95.375%.

关键词： Fault assessment Feature fusion Feature selection Feature sensitivity

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基于数据挖掘技术的税务风险检测方法评述

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engineering 2024年第3期34卷 43-59页

作者： Qinghua Zheng Yiming Xu Huixiang Liu Bin Shi Jiaxiang Wang Bo Dong School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an 710049China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong UniversityXi’an 710049China School of Distance Education Xi’an Jiaotong UniversityXi’an 710049China

Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair *** recent years,tax risk detection,driven by information technology such as data mining and artificial intelligence,has received extensive *** promote the high-quality development of tax risk detection methods,this paper provides the first comprehensive overview and summary of existing tax risk detection methods *** specifi-cally,it first discusses the causes and negative impacts of tax risk behaviors,along with the development of tax risk *** then focuses on data-mining-based tax risk detection methods utilized around the *** on the different principles employed by the algorithms,existing risk detection methods can be divided into two categories:relationship-based and non-relationship-based.A total of 14 risk detection methods are identified,and each method is thoroughly explored and ***,four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed,including the difficulty of integrating and using fiscal and tax fragmented knowledge,unexplainable risk detection results,the high cost of risk detection algorithms,and the reliance of existing algorithms on labeled *** investigating these issues,it is concluded that knowledge-guided and datadriven big data knowledge engineering will be the development trend in the field of tax risk in the future;that is,the gradual transition of tax risk detection from informatization to intelligence is the future development direction.

关键词： Tax risk detection data mining knowledge guide Informatization Intellectualization

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