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arXiv 2024年

作者： Zhang, Xinrui Cai, Ailong Wang, Shaoyu Wang, Linyuan Zheng, Zhizhong Li, Lei Yan, Bin The Henan Key Laboratory of Imaging and Intelligent Processing Information Engineering University Zhengzhou450001 China The Key Laboratory of Optoelectronic Technology and Systems Ministry of Education Chongqing University Chongqing400044 China

Metal artifacts in computed tomography (CT) imaging pose significant challenges to accurate clinical diagnosis. The presence of high-density metallic implants results in artifacts that deteriorate image quality, manifesting in the forms of streaking, blurring, or beam hardening effects, etc. Nowadays, various deep learning-based approaches, particularly generative models, have been proposed for metal artifact reduction (MAR). However, these methods have limited perception ability in the diverse morphologies of different metal implants with artifacts, which may generate spurious anatomical structures and exhibit inferior generalization capability. To address the issues, we leverage visual-language model (VLM) to identify these morphological features and introduce them into a dual-domain CLIP-assisted residual optimization perception model (DuDoCROP) for MAR. Specifically, a dual-domain CLIP (DuDoCLIP) is fine-tuned on the image domain and sinogram domain using contrastive learning to extract semantic descriptions from anatomical structures and metal artifacts. Subsequently, a diffusion model is guided by the embeddings of DuDoCLIP, thereby enabling the dual-domain prior generation. Additionally, we design prompt engineering for more precise image-text descriptions that can enhance the model’s perception capability. Then, a downstream task is devised for the one-step residual optimization and integration of dual-domain priors, while incorporating raw data fidelity. Ultimately, a new perceptual indicator is proposed to validate the model’s perception and generation performance. With the assistance of DuDoCLIP, our DuDoCROP exhibits at least 63.7% higher generalization capability compared to the baseline model. Numerical experiments demonstrate that the proposed method can generate more realistic image structures and outperform other SOTA approaches both qualitatively and quantitatively. Copyright © 2024, The Authors. All rights reserved.

关键词： Computerized tomography

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intelligent Test Paper Generation Algorithm Integrating Normal Distribution and Parallel Genetic Algorithm

Intelligent Test Paper Generation Algorithm Integrating Norm...

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intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Chuangye Liu Guimin Huang Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Genetic algorithms have been widely used in intelligent test paper generation systems. However, traditional genetic algorithms cannot ensure that the difficulty of test questions is normally distributed, and are prone to falling into local optimal solutions. To address the above problems, we proposed an intelligent test paper generation algorithm that combines normal distribution and parallel genetic algorithms. In the population initialization stage, we use the cumulative distribution function (CDF) of the normal distribution to initialize the population. In the subsequent genetic operations, we designed a directed mutation mechanism and added KL divergence as a penalty number in the design of the fitness function to ensure that the difficulty of the test questions is normally distributed. At the same time, in order to speed up the efficiency of the algorithm and escape from the local optimal solution, an adaptive migration strategy was introduced. Experimental results show that the difficulty of the test questions generated by this algorithm is normally distributed, and the algorithm performance is better than other algorithms.

关键词： Human computer interaction Heuristic algorithms Gaussian distribution Genetics Genetic algorithms Distribution functions Convergence

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Building Semantic Segmentation Through an Ensemble Architecture of UNet and Graph Convolution Networks 22

Building Semantic Segmentation Through an Ensemble Architect...

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Proceedings of the 2022 6th International Conference on Electronic Information technology and Computer Engineering

作者： Yuxin Shang Hao Cuib Haitao Guo Shandong Institute of Commerce and Technology China Shandong Institute of Petroleum and Chemical Technology Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9781450397148

Extracting building from remote sensing images remains a challenge because some obstacles, such as trees, cars, farmland, and shadows, disturb the identification accuracy. In recnet years, deep convolution neural networks have shown significant improvements in computer vision, however it is still difficult to extract irregular and small buildings. To develop the extraction accuracy of building, we propose a deep learning model, which combined UNet and graph convolution network, to capture the long-range information. Seven deep convolution neural networks were used on the Massachusetts Buildings Dataset for comparison. And the proposed model, which performed best among these compared models, achieved a building Intersection over Union (IOU) of 71.10%, mean IOU of 81.88%, recall of 81.90%, precision 84.36% and F1 score of 83.11%. This result indicated that the proposed model can accurately extract building objects. The visualization results showed that the proposed model performs better in extracting irregular and small buildings and caneffectively extracts buildings from high-resolution remote-sensing images.

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Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Ya Zhou Chen Chen Guimin Huang School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

In the research methods of sentence similarity, sentence similarity is often calculated from the semantic aspect, while the influence of syntactic structure is ignored. We propose an enhanced knowledge language representation model (ExtKBRCNN) based on CNN and Bi-GRU, which effectively uses the fine-grained word relations in the knowledge base to evaluate semantic similarity and models the relationship between knowledge structure and text structure. In order to make full use of the syntactic information of the sentence, we also propose a dependency tree kernel-based method (Dep-SIF), which combines syntactic information and semantic features to evaluate syntactic similarity. Finally, we propose a comprehensive model that integrates semantic and syntactic information to comprehensively evaluate sentence similarity. Experimental results show that the accuracy of the model on the MRPC dataset is 77.63% and the F1 value is 83.90%.

关键词： Knowledge engineering Accuracy Semantics Knowledge based systems Syntactics Natural language processing Software engineering

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Using Rough Sets to Improve the High-dimensional Data Anomaly Detection Method Based on Extended Isolation Forest 26

Using Rough Sets to Improve the High-dimensional Data Anomal...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Hanlin Zhou, Jiantao Li, Hua Inner Mongolia University College of Computer Science Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods. © 2023 IEEE.

关键词： Anomaly detection

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Flexible and highly-sensitive pressure sensor based on controllably oxidized MXene

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InfoMat 2022年第9期4卷 60-71页

作者： Yanan Ma Yongfa Cheng Jian Wang Si Fu Mengjun Zhou Yue Yang Baowen Li Xin Zhang Ce-Wen Nan State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center of Smart Materials and Devices&International School of Materials Science and EngineeringWuhan University of TechnologyWuhanChina School of Mathematics Physics and Optoelectronic EngineeringHubei University of Automotive TechnologyShiyanChina Center for Nanoscale Characterization&Devices(CNCD) Wuhan National Laboratory for Optoelectronics(WNLO)Huazhong University of Science and Technology(HUST)WuhanChina Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefeiChina State Key Lab of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijingChina

Conductive Ti_(3)C_(2)T_(x)MXenes have been widely investigated for the construction of flexible and highly-sensitive pressure *** the inevitable oxidation of solution-processed MXene has been recognized,the effect of the irreversible oxidation of MXene on its electrical conductivity and sensing properties is yet to be ***,we construct a highly-sensitive and degradable piezoresistive pressure sensor by coating Ti_(3)C_(2)T_(x)MXene flakes with different degrees of in situ oxidation onto paper substrates using the dipping-drying *** situ oxidation can tune the intrinsic resistance and expand the interlayer distance of MXene *** partially oxidized MXene-based piezoresistive pressure sensor exhibits a high sensitivity of 28.43 kPa^(-1),which is greater than those of pristine MXene,over-oxidized MXene,and state-of-the-art paper-based pressure ***,these sensors exhibit a short response time of 98.3 ms,good durability over 5000 measurement cycles,and a low force detection limit of 0.8 ***,MXene-based sensing elements are easily degraded and environmentally *** MXene-based pressure sensor shows promise for practical applications in tracking body movements,sports coaching,remote health monitoring,and human–computer interactions.

关键词： adjustable sensitivity in situ oxidation interlayer distance MXene pressure sensor

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A Convolutional Knowledge Tracking Model with Forgetting Behavior and Graph Embedding

A Convolutional Knowledge Tracking Model with Forgetting Beh...

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International Conference on Electronics and Information technology (EIT)

作者： Guimin Huang Zhiqing Huang School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

With the prevalence of various intelligent educational systems, it is imperative to uncover learners' proficiency in mastering knowledge points through their prior practice circumstances. Knowledge tracking is an extremely beneficial instrument in this regard. Among them, Convolutional Knowledge Tracking (CKT) has demonstrated excellent performance in numerous KT tasks, while it does not take into account the phenomenon of forgetting and the interconnection between knowledge points. In order to tackle these problems, we propose a model called FGE-CKT, which integrates forgetting behavior and graph embedding with CKT. Our model addresses the drawbacks of forgetting characteristics by modelling three key factors affecting students' forgetting behaviors through a fully connected neural net: repeated knowledge learning times, adjacent learning interval and interval between visits to repeat knowledge. At the same time, convolutional neural networks are used to simultaneously extract and incorporate connections between knowledge points into the model's input portion. It was confirmed by experiments on two well-established datasets that FGE-CKT achieved significant improvements in AUC performance.

关键词： Knowledge engineering Instruments Neural networks Predictive models Vectors Complexity theory Convolutional neural networks Information technology

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Hierarchical Spatio-temporal Decoupling for Text-to- Video Generation

Hierarchical Spatio-temporal Decoupling for Text-to- Video G...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhiwu Qing Shiwei Zhang Jiayu Wang Xiang Wang Yujie Wei Yingya Zhang Changxin Gao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group Fudan University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Despite diffusion models having shown powerful abilities to generate photorealistic images, generating videos that are realistic and diverse still remains in its infancy. One of the key reasons is that current methods intertwine spatial content and temporal dynamics together, leading to a notably increased complexity of text-to-video generation (T2V). In this work, we propose HiGen, a diffusion model-based method that improves performance by decoupling the spatial and temporal factors of videos from two perspectives, i.e., structure level and content level. At the structure level, we decompose the T2V task into two steps, including spatial reasoning and temporal reasoning, using a unified denoiser. Specifically, we generate spatially coherent priors using text during spatial reasoning and then generate temporally coherent motions from these priors during temporal reasoning. At the content level, we extract two subtle cues from the content of the input video that can express motion and appearance changes, respectively. These two cues then guide the model's training for generating videos, enabling flexible content variations and enhancing temporal stability. Through the decoupled paradigm, HiGen can effectively reduce the complexity of this task and generate realistic videos with semantics accuracy and motion stability. Extensive experiments demonstrate the superior performance of HiGen over the state-of-the-art T2V methods. We have released our source code and models.

关键词： Training Source coding Semantics Spatial coherence Cognition Stability analysis Complexity theory

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Multi-frame Temporal Modeling for Multi-object Tracking in Satellite Video

Multi-frame Temporal Modeling for Multi-object Tracking in S...

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Signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Bingqian Chai Tingting Qiao Baorong Xie Jue Wang Wenchao Liu National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China Shanghai Aerospace Electronic Technology Institute Shanghai China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

With the advancement of remote sensing technology, video satellites have played an increasingly important role in both military and civilian domains, and they are widely used in areas such as military guidance, intelligent transportation, and disaster response. Multi-object tracking (MOT) in satellite video is a crucial research topic in these fields and has made certain progress. However, due to the difficulty of tracking objects in complex scene and low resolution, current MOT methods face issues of low accuracy in satellite video. In this work, we propose a joint-detection-and-tracking method called multi-frame temporal MOT (MFT-MOT). Firstly, a heatmap guided multi-frame temporal modeling method is introduced to extract features with temporal semantic information while suppressing background noise. Then, we utilize a weights-shared encoder-decoder network to facilitate interaction of temporal and spatial information and capture inter-frame object motion tracks at low resolution. Experiments on SAT-MTB dataset shows the effectiveness of our method.

关键词： Heating systems Satellites Tracking Semantics Transportation Feature extraction Trajectory Military satellites Spatial resolution Remote sensing

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A Mixed Method for Estimating Parameters of Three-Parameter Weibull Distribution 9

A Mixed Method for Estimating Parameters of Three-Parameter ...

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9th International Symposium on System Security, Safety, and Reliability, ISSSR 2023

作者： Jiang, Renyan Li, Fengping Xue, Wei Li, Xiao Zhang, Kunpeng Institute of Laser and Optoelectronic Intelligent Manufacturing Wenzhou University Wenzhou325035 China Wenzhou University China International Science & Technology Cooperation Base for Laser Processing Robotics Wenzhou325035 China Wenzhou University Provincial Key Laboratory of Laser Processing Robotics College of Mechanical and Electrical Engineering Wenzhou325035 China

ISBN: (纸本)9798350302479

The three-parameter Weibull distribution has been widely used for modeling component lifetime and material strength. The parameter estimates obtained from the maximum likelihood estimation method (MLE) and maximum product of spacing (MPS) method can be biased or inexistent. Though many other approaches have been developed, none is always efficient. Therefore, it is still a challenging issue to develop more efficient estimation methods. This paper aims to address this issue by proposing a mixed approach. The proposed approach is based on a modified Weibull transformations and involves three main steps. The first step determines two reference points from the empirical distribution function. In the second step, the location parameter is estimated based on the ratios of the difference between the reference point and data points on the modified Weibull probability paper (MWPP) plot. The third step estimates the shape and scale parameters based on the linear relation of the MWPP plot. The accuracy and robustness of the proposed approach is illustrated by a numerical experiment and its usefulness is illustrated by a real-world example. The results shows that the proposed approach provides more accurate and robust estimates than the MLE and MPS. © 2023 IEEE.

关键词： Parameter estimation

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