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IEEE International Conference on Big Knowledge (ICBK)

作者： Qiang Chen Haining Meng Yihao Bao Mingsong Yang Xinyu Tong Qin Zhao Muhammad Bilal Khan Xinhong Hei School of Computer Science and Engineering Xi’an University of Technology Xi’an China School of Computer Science and Engineering Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an University of Technology Xi’an China School of Civil Engineering and Architecture Xi’an University of Technology Xi’an China

ISBN: (数字)9798331508821

ISBN: (纸本)9798331508838

Event extraction in rail transit plays a vital role in all stages of design, construction, and final acceptance. Event extraction is a key part of building event logic graph (ELG). However, the difficulty in obtaining semantic features and event local dependencies poses a challenge to accurate event extraction, which hinders the informatization and intelligent development of rail transit projects. To address these challenges, we proposes an event extraction method that fuses semantic and local dependency features. The study initially preprocesses the rail transit design codes dataset and performs sequence labeling. Then, the event trigger words are mined by combining the term frequency-inverse document frequency (TF-IDF) and minimum edit distance (MED) algorithms. Based on the semantic similarity measurement method and the trigger word mining results, the features representation of event semantic information is enhanced by using the Bidirectional Encoder Representations from Transformers (BERT) model and Bi-directional Long Short-Term Memory (BiLSTM) model. At the same time, event arguments are mined and a local dependency graph is generated based on their mining results, and then the enhanced local dependency features are obtained by using the graph convolutional network (GCN). Finally, the event semantic features and local dependency features are fused, and the conditional random field (CRF) is used to predict the labels to obtain the event extraction results. Through the comparison with five baseline methods on the datasets of rail transit design codes, it is found that the Precision, Recall, and F1 score of the proposed method are improved, with an average improvement of 9.604% on Precision, 9.342% on Recall, and 9.730% on F1 score, respectively. The contribution of the sub-modules to the overall performance is further evaluated through ablation experiments, which confirms the effectiveness and feasibility of the proposed method.

关键词： Rails Codes Semantics Bidirectional control Knowledge graphs Feature extraction Transformers Conditional random fields Logic Labeling

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Causality-driven Hierarchical Structure Discovery for Reinforcement Learning

arXiv

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arXiv 2022年

作者： Peng, Shaohui Hu, Xing Zhang, Rui Tang, Ke Guo, Jiaming Yi, Qi Chen, Ruizhi Zhang, Xishan Du, Zidong Li, Ling Guo, Qi Chen, Yunji SKL of Processors Institute of Computing Technology CAS China University of Chinese Academy of Sciences China Cambricon Technologies China Department of Computer Science and Engineering Southern University of Science and Technology China SKL of Computer Science Institute of Software CAS China University of Science and Technology of China China

Hierarchical reinforcement learning (HRL) effectively improve agents’ exploration efficiency on tasks with sparse reward, with the guide of high-quality hierarchical structures (e.g., subgoals or options). However, how to automatically discover high-quality hierarchical structures is still a great challenge. Previous HRL methods can hardly discover the hierarchical structures in complex environments due to the low exploration efficiency by exploiting the randomness-driven exploration paradigm. To address this issue, we propose CDHRL, a causality-driven hierarchical reinforcement learning framework, leveraging a causality-driven discovery instead of a randomness-driven exploration to effectively build high-quality hierarchical structures in complicated environments. The key insight is that the causalities among environment variables are naturally fit for modeling reachable subgoals and their dependencies and can perfectly guide to build high-quality hierarchical structures. The results in two complex environments, 2D-Minecraft and Eden, show that CDHRL significantly boosts exploration efficiency with the causality-driven paradigm. Copyright © 2022, The Authors. All rights reserved.

关键词： Efficiency

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Simplified Dielectric Property Determination for Wearable Applications Using Dual CPW Lines

Simplified Dielectric Property Determination for Wearable Ap...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Jialiang Ni Kwok L. Chung Liwen Liang Jingshu Sun Wenbiao Li Xiaotian Liu Tongtong Ouyang Zhibin Chen School of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangzhou China School of Intelligent Manufacturing and Electrical Engineering Guangzhou Institute of Science and Technology Guangzhou China School of Civil Engineering and Architecture Guangzhou Institute of Science and Technology Guangzhou China School of Computer Science and Engineering Huizhou University Huizhou China

ISBN: (数字)9798331512620

ISBN: (纸本)9798331512637

This paper presents an enhanced dual transmission-line method for evaluating the dielectric properties of known and unknown materials, significantly improving upon traditional techniques. The proposed method simplifies the experimental setup by eliminating the need for constructing transmission lines or additional processing on unknown materials, utilizing a straightforward simulation model created with the EM simulator CST and requiring only low-cost experimental equipment. It operates independently of prior knowledge concerning the substrate dielectric constant, streamlining data processing and saving time. Furthermore, the method demonstrates high accuracy in comparison to reference values. Applied in this study, it verified the dielectric properties of unknown cowhide leather and two commercial substrates, RO3010 and RT5880. The computed root-mean-square errors (RMSEs) were 24.1% for RT5880 and 3% for RO3010, confirming the method's effectiveness. Overall, this improved approach provides a reliable and efficient alternative for material characterization in dielectric property determination.

关键词： Dielectric constant Accuracy Transmission line matrix methods Materials reliability Data processing Transmission line measurements Propagation losses Dielectrics Coplanar waveguides Substrates

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Numerical Feature Preprocessing Method for Daily Solar Radiation Pattern Classification 15th

Numerical Feature Preprocessing Method for Daily Solar Radia...

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15th International Conference on computer science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Li, You Wang, Yafei Zheng, Ying Qian, Hui Zhou, Weisheng Dual-Carbon Research Center Hangzhou City University Hangzhou310015 China Asia-Japan Research Institute Ritsumeikan University Ibaraki567-8570 Japan School of Civil Engineering and Architecture Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science and Information Technologies Frostburg State University FrostburgMD21532 United States College of Policy Science Ritsumeikan University Ibaraki567-8570 Japan

ISBN: (纸本)9789819724468

One challenging issue in daily solar radiation classification involves addressing the geometric differences in daily solar radiation patterns caused by geographic location and seasonal influences. A standardization method based on matrix transformation has been proposed to eliminate the daily geometric variations of solar radiation using classification for daily patterns of solar radiation. This method was tested using measurements taken at the Tokyo monitoring station from 2000 to 2021. Through the implementation of the proposed classification algorithm, this paper discerned twelve categories of daily clearness index distributions. The suggested classification framework has the potential to assist in the development of solar radiation transformation systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Solar irradiance

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Simulation Optimization Method for Intelligent Compaction Construction of Railway Subgrade Oriented Towards Quality Control

SSRN

引用

SSRN 2024年

作者： Ma, Zhaoxi Zhao, Qin Jia, Hongfei Sheng, Yiheng Yang, Mingsong Gu, Jingtian Zhu, Yiyun Hei, Xinhong Department of Civil Engineering and Architecture Xi’an University of Technology Xi’an710048 China Department of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China

This study, conducted in the context of railway subgrade construction, focuses on quality control and explores an effective method by developing a simulation model and a real-time optimization algorithm. A real-time optimization algorithm was initially utilized to optimize the compaction scheme based on Intelligent Compaction (IC) technology. Subsequently, a comprehensive construction simulation process was established, defining features such as entity arrival patterns, service modes, queueing rules, and service processes, ensuring the accuracy and reliability of the simulation process. Simulation experiments were then conducted to determine the globally optimal compaction construction scheme. Comparison with the original construction scheme revealed that the optimal compaction scheme met quality requirements and improved construction efficiency by nearly 11.4%. Additionally, it facilitated synergistic operations with other construction activities. Thus, this study's quality control method offers a scientific and efficient management tool and decision-making basis for railway subgrade construction, possessing significant practical and promotional value. © 2024, The Authors. All rights reserved.

关键词： Discrete event simulation

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Simulation Optimization Method for Intelligent Compaction Construction of Railway Subgrade Oriented Towards Quality Control

SSRN

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SSRN 2024年

关键词： Discrete event simulation

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Nonlinear Time History Analysis for the Different Column Orientations under Seismic Wave Synthetic Approach

引用

World Journal of Engineering and technology 2024年第3期12卷 587-616页

作者： Mo Shi Peng Wang Xiaoyan Xu Yeol Choi School of Architecture Kyungpook National University Daegu Republic of Korea School of Computer Science and Technology Shandong University Qingdao China HaXell Elevator Co. Ltd. Shanghai China

The significant impact of earthquakes on human lives and the built environment underscores the extensive human and economic losses caused by structural collapses. Over the years, researchers have focused on improving seismic design to mitigate earthquake-induced damages and enhance structural performance. In this study, a specific reinforced concrete (RC) frame structure at Kyungpook National university, designed for educational purposes, is analyzed as a representative case. Utilizing SAP 2000, the research conducts a nonlinear time history analysis to assess the structural performance under seismic conditions. The primary objective is to evaluate the influence of different column section designs, while maintaining identical column section areas, on structural behavior. The study employs two distinct seismic waves from Abeno (ABN) and Takatori (TKT) for the analysis, comparing the structural performance under varying seismic conditions. Key aspects examined include displacement, base shear force, base moment, joint radians, and layer displacement angle. This research is anticipated to serve as a valuable reference for seismic restraint reinforcement work on RC buildings, enriching the methods used for evaluating structures through nonlinear time history analysis based on the synthetic seismic wave approach.

关键词： Nonlinear Time History Analysis Nonlinear Dynamic Analysis Seismic Wave Synthetic Seismic Restraint RC Frame Structure Column Orientation

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Vulnerability Detection Based on Unified Code Property Graph 20th

Vulnerability Detection Based on Unified Code Property Graph

引用

Proceedings of the 20th Web Information Systems and Applications Conference, WISA 2023

作者： Li, Wei Li, Xiang Feng, Wanzheng Jin, Guanglu Liu, Zhihan Jia, Jing College of Computer Science and Technology Harbin Engineering University Harbin150001 China College of Communication Engineering Jilin University Jilin130012 China Faculty of Arts Design and Architecture School of Built Environment The University of New South Wales SydneyNSW2052 Australia

ISBN: (纸本)9789819962211

As the number of source codes grows rapidly, detecting source code vulnerabilities in current software has become an important study. Most current deep learning-based vulnerability detection technologies treat source code as a sequence, which loses the source code’s structural information, leading to many false positives in the detection results. We propose a novel source code vulnerability detection model, named UCPGVul, based on the Unified Code Property Graph (UCPG). A new graph representation, UCPG, is proposed to extract semantic features from the source code. By extracting features from UCPG, our proposed UCPGVul model can capture more vulnerability features. Experimental results on a publicly available dataset show that UCPGVul can achieve more accurate and stable detection results compared to five state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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A Lottery-based Spherical Evolution Algorithm with Elite Retention Strategy 14

A Lottery-based Spherical Evolution Algorithm with Elite Ret...

引用

14th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2022

作者： Li, Jiayi Zhang, Zihang Lei, Zhenyu Yi, Junyan Gao, Shangce University of Toyama Faculty of Engineering Toyama930-8555 Japan Beijing University of Civil Engineering and Architecture Department of Computer Science and Technology Beijing100044 China

ISBN: (数字)9781665461696

ISBN: (纸本)9781665461696

Spherical evolution (SE) is a recently proposed meta-heuristic algorithm. Its special search approach has been proved to be very effective in exploring the search space. SE is very powerful for optimization, but still has room for improvement due to some promising solutions usually fail to survive into the next generation. To alleviate this issue, we innovatively design a novel lottery-based elite retention strategy and propose a lottery elite spherical evolution algorithm (LESE). To verify the effectiveness of the proposed LESE, we experimentally compare it with the original SE algorithm and other representative meta-heuristics algorithms. We use 30 benchmark functions from the IEEE CEC2017 benchmark as the test set for our experiments. The effectiveness of LESE is demonstrated by analyzing the experimental results from perspectives of solution accuracy, convergence speed, solution distribution, and search dynamics. © 2022 IEEE.

关键词： Spheres

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Point-Correlate Adversarial Transformer for Unsupervised Multivariate Time Series Anomaly Detection

Point-Correlate Adversarial Transformer for Unsupervised Mul...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Huan Li Xiangjie Kong Guojiang Shen Xiaoran Yan Yao Yang Mario Collotta School of Computer Science and Technology Zhejiang University of Technology Hangzhou China Zhejiang Lab Hangzhou China Faculty of Engineering and Architecture Kore University of Enna Enna Italy

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Multivariate time series anomaly detection plays a crucial role in industrial production. However, the inherent complexity and randomness of time series pose significant challenges. Furthermore, existing detection methods struggle to provide reliable explanations for outliers. To address these issues, this paper presents an unsupervised multivariate time series anomaly detection model named Point-Correlate Adversarial Transformer (PCAT). In this work, we leverage Transformer networks to capture the underlying correlations between different points in a time series and reconstruct the original sequence. By analyzing the correlation differences and reconstruction errors, we identify anomalies at the point level. Our model incorporates an adversarial structure, enabling unsupervised learning and enhancing the learning capability and robustness of the detection network. Experimental evaluations on four real-world datasets demonstrate the superiority of our approach over other state-of-the-art models in terms of detection delay and accuracy.

关键词： Training Correlation Accuracy Federated learning Time series analysis Production Transformers

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