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Automatic picking of multi-mode surface-wave dispersion curves based on machine learning clustering methods

COMPUTERS & GEOSCIENCES 2021年 153卷

作者： Wang, Zhinong Sun, Chengyu Wu, Dunshi China Univ Petr East China Sch Geosci Qingdao 266580 Peoples R China PetroChina Res Inst Petr Explorat & Dev Northwest Lanzhou 730020 Peoples R China

The surface-wave analysis method is widely adopted to build near-surface shear-wave velocity structure. Automatic picking of surface-wave dispersion curves is a problem that needs to be solved, especially when dealing with a large amount of data. In this paper, we proposed an automatic picking method of multi-mode surface-wave dispersion curves based on unsupervised machine learning methods. Firstly, seismic data is transformed to two-dimensional dispersion image by dispersion imaging method. The discrete dispersion image points are separated into two clusters: dispersion energy and background noise by Gaussian mixture model clustering method. Then the dispersion energy points are projected into frequency-velocity domain, and are distinguished as different surface-wave modes by the density-based spatial clustering of applications with noise algorithm. Finally, multi-mode dispersion curves are obtained by searching for the local peaks in different mode areas of surface waves. Particle filter is used to smooth the picked dispersion curves to eliminate the influence of noise. The synthetic tests show that the automatically picked dispersion curves can match the theoretical dispersion curves well. We also applied the proposed method to the picking of dispersion curves of two field data. The automatically picked dispersion curves are applied to the subsequent inversion. In the second field data, the one-dimensional inversion results are interpolated to obtain the near-surface three-dimensional velocity model of the work area. The inversion results are in good agreement with borehole data, which further proves the accuracy and efficiency of the automatic picking method for multi-mode dispersion curves.

关键词： Surface waves Dispersion curves Machine learning clustering algorithm Automatic picking

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Skeleton-Based Action Recognition with Improved Graph Convolution Network 1

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15th Chinese Conference on Biometric Recognition (CCBR)

作者： Yang, Xuqi Zhang, Jia Qin, Rong Su, Yunyu Qiu, Shuting Yu, Jintian Ge, Yongxin Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China

ISBN: (数字)9783030866082

ISBN: (纸本)9783030866082;9783030866075

Most previous skeleton-based action recognition methods ignore weight information of joints and data features beyond labels, which is harmful to action recognition. In this paper, we propose a skeleton-based action recognition with improved Graph Convolution Network, which is based on Spatial Temporal Graph Convolutional Network (STGCN). And we add a predictive cluster network, weight generation networks on it. The model uses K-means algorithm to cluster and get the data information beyond the labels. Besides, each cluster traines weight generation networks independently. To find the best clusters, we propose a evaluation criterion with less computational effort. We perform extensive experiments on the Kinetics dataset and the NTU RGB+D dataset to verify the effectiveness of each network of our model. The comparison results show that our approach achieves satisfactory results.

关键词： clustering algorithm Skeleton-based action recognition Deep learning

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An autonomous vehicles’ test case extraction method: Example of vehicle-to-pedestrian scenarios

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Heliyon 2025年第1期11卷 e41073页

作者： Tan, Zhengping Wang, Qian Hu, Wenhao Li, Pingfei Shi, Liangliang Feng, Hao School of Automobile &Transportation Xihua University Chengdu 610039 China Sichuan Xihua Jiaotong Forensic Science Center Chengdu 610039 China State Administration for Market Regulation Defective Product Recall Technical Center (DPRC) Beijing 100101 China China Automotive Engineering Research Institute Chongqing 401122 China Key Lab of Forensic Science Ministry of Justice China (Academy of Forensic Science) Shanghai 200063 China

Testing autonomous vehicles (AVs) in hazardous scenarios is a crucial technical approach to ensure their safety. A key aspect of this process is the generation of hazard scenarios. In general, such scenarios are generated through cluster analysis of traffic accident data. However, this approach may not fully capture the criticality of the generated scenarios, as it tends to emphasize the statistical characteristics of the data rather than its real-world applicability. This paper proposes a novel method to enhance scenario adaptation by integrating quantization weights with a new clustering algorithm. These weights, representing the correlation between scenario elements and the AV system, are calculated using fuzzy comprehensive evaluation (FCE). The proposed method is applied to 1044 pedestrian accident cases in China, resulting in the identification of nine categories of typical scenarios and corresponding test schemes for both the perception and decision-making systems of AVs. The results show that the new method increases the proportion of critical scenarios by 17.4 % and 13.6 %, respectively, compared to traditional methods. Overall, the critical scenarios generated in this paper can significantly improve the testing efficiency and safety of AVs. © 2024 The Authors

关键词： Autonomous vehicles clustering algorithm Decision-making system Fuzzy comprehensive evaluation Perception system Test scenario

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Suggesting Waypoints to Autonomous Surface Vessels for Effective Emergency Response Using Machine Learning

Suggesting Waypoints to Autonomous Surface Vessels for Effec...

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OCEANS Conference

作者： Lekamge, Udeera Harischandra, Jayani Informat Inst Technol Dept Comp Colombo Sri Lanka

ISBN: (纸本)9780692935590

With the advancements in the maritime industry, which delivers almost 90 percent of the world trade, the frequency of maritime activities has drastically increased resulting a major concern in maritime safety. A significant 30 percent of maritime accidents are caused due to bad weather conditions, for instance sea storms and strong winds created due to high turbulence and waves. The deaths and casualties caused due to these accidents would have been minimized if there was a mechanism for efficient emergency response. Autonomous Surface Vessels (ASVs) have been used for several disaster mitigation and recovery operations in hurricanes, earthquakes and tsunami. ASVs are comparatively cheap and safe to be deployed on to hazardous zones in the deep sea due to their long term marine presence. A more efficient way for emergency response by the ASVs would be, the ability to predict a location where there is a possibility for an accident to take place and position itself such that it could effectively respond to the emergency. Hence the author is proposing an optimal solution using machine learning techniques to suggest waypoints to ASVs for effective emergency response on human operated surface vessels.

关键词： Unmanned Surface Vehicles Maritime Accidents Maritime Safety Marine Weather Emergency Response Machine Learning clustering algorithm

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The identification of power IoT devices on differential constellation trajectory map 13

The identification of power IoT devices on differential cons...

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13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Li, Qinyuan Sun, Xin Lv, Pang Sun, Changhua State Grid Zhejiang Elect Power Co Ltd Res Inst Hangzhou 310014 Zhejiang Peoples R China

ISBN: (纸本)9781665438926

As a long-distance wireless transmission technology based on spread spectrum, LoRa is a hot research object in the field of power Internet of Things. To solve the problem of terminal authentication, this paper proposes a LoRa device identification method based on a differential constellation trajectory map, which skillfully transforms the radiofrequency fingerprint feature matching problem into the image processing problem. This method first obtains the constellation locus of the received signal. It performs differential processing and then USES the clustering algorithm to get the constellation locus's clustering center. It then calculates the Euclidean distance between the clustering centers of two devices to obtain the similarity between them and USES this as a basis for equipment identification. The experimental results show that the LoRa device recognition method based on differential constellation trajectory map can effectively identify five LoRa transmission modules and have a high recognition accuracy even in low SNR.

关键词： LoRa Radio frequency fingerprint Differential constellation trajectories clustering algorithm

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Development of decision support tool for clustering urban regional risk based on R-ArcGIS Bridge

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APPLIED SOFT COMPUTING 2021年 110卷

作者： Zhao, Ming Liu, Xiang Sun, Zengfeng Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Beijing Nutrichem Co Ltd Beijing 102206 Peoples R China China Acad Urban Planning & Design Minist Housing & Urban Rural Dev Beijing 100044 Peoples R China

Cities with dense population are susceptible and vulnerable to natural disasters and man-made accidents, making regional risk analysis thus significant for urban public safety. Although risk maps are able to present the broad characteristics of regional risk spatial distribution, quantitative identification of risk clusters in urban areas is still a challenge. In this context, we design and develop a user-friendly customized ArcGIS add-in in this study. In particular, a novel technical routing is proposed for customizing ArcGIS add-in tool based on R-ArcGIS Bridge. The design, architecture and implementation of the tool as well as its core functional modules are introduced. Moreover, R scripts are developed to implement K-means algorithm and Gap statistics validity index for clustering regional risk, respectively. Based on a case study of a typical urban district in China, we introduce the add-in's functionalities as well as its related decision-making procedures. Results successfully obtained provide evidence that the tool is able to partition regional risk clusters and estimate the optimal number of clusters in urban areas. The simple loosely-coupled architecture also suggests a promising future for embedding some novel geospatial clustering algorithms to extend its capabilities in the next step. This work offers new insights on promoting future urban regional risk management with the use of GIS and clustering algorithms, and it provides a valuable demonstration on extending and enhancing the analysis capacity of GIS by harnessing its power with the statistical analysis capability of R packages through R-ArcGIS Bridge. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Regional risk analysis clustering algorithm Add-in R-ArcGIS Bridge Urban public safety

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Failure Mode and Effect Analysis using an integrated approach of clustering and MCDM under pythagorean fuzzy environment

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JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES 2021年 72卷

作者： Shahri, Majid Mardani Jahromi, Abdolhamid Eshraghniaye Houshmand, Mahmoud Sharif Univ Technol Dept Ind Engn Tehran Iran

Failure Mode and Effect Analysis (FMEA) is an effective risk analysis and failure avoidance approach in the design, process, services, and system. With all its benefits, FMEA has three limitations: failure mode risk assessment and prioritization, complex FMEA worksheets, and difficult application of FMEA tables. This paper seeks to overcome the shortcomings of FMEA using an integrated approach based on a developed Pythagorean fuzzy (PF) k-means clustering algorithm and a popular MCDM method called PF-VIKOR. In the first step, Pythagorean fuzzy numbers (PFNs) were used to collect Severity (S), Occurrence (O), and Detection (D) factors for failure modes to incorporate uncertainty and fuzziness into subjective judgments. Afterward, failure modes were clustered by developing a novel k-means clustering algorithm that accepts PFNs as input. Finally, the PF-VIKOR approach was used to analyze the ordering of cluster risks. The proposed approach was implemented in the dehydration unit of an Iranian gas refinery and the results were compared with the traditional FMEA. The findings showed the flexibility and applicability of the proposed approach in addressing real-world problems. This research provides two key contributions: (1) designing a PFN-based k-means clustering algorithm that tackles FMEA limitations and (2) using the PF-VIKOR method for prioritizing and evaluating failure mode clusters.

关键词： Failure mode and effect analysis Pythagorean fuzzy numbers clustering algorithm PF-VIKOR Gas refinery

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Risk Evaluation of Underway POL Replenishment at Sea Based on clustering algorithm

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS & STATISTICS 2013年第13期43卷 85-91页

作者： Mu, Xin Guo, Jikun Zhao, Chunjiang Niu, Yongjie Logist Engn Univ Dept Management Sci & Engn Chongqing 401311 Peoples R China

This paper presents a new risk-evaluation model for underway POL replenishment at sea, which is based on clustering algorithm. Although there are many studies on risks involved in POL replenishment at sea, the majority of them are mainly focused on the risks caused by sea conditions. In reality, there are many other influencing factors, such as thunder and lighting, dense fog, and so on. Thus, the results obtained in those previous studies are incomplete with many uncertainties still existing. By taking several major environment factors into consideration, this paper employs clustering algorithm to analyze data in simulated experiments, and puts forward a new risk-evaluation model for underway POL replenishment at sea.

关键词： risk evaluation, underway POL replenishment at sea clustering algorithm

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Optimised Fusion Model for Meeting Sulphur Abatement Standards in Shipping Industry 45

Optimised Fusion Model for Meeting Sulphur Abatement Standar...

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45th Annual International IEEE-Computer-Society Computers, Software, and Applications Conference (COMPSAC)

作者： Hu, Yuting Zhou, Shikun Sanders, David Zhang, Weicong Yang, Linda Univ Portsmouth Sch Comp Portsmouth Hants England Zhejiang Wanli Univ Ningbo Zhejiang Peoples R China

ISBN: (纸本)9781665424639

The International Maritime Organization (IMO) enforced stricter sulphur abatement regulations since shipping emission has become one of the most major cause of the atmospheric pollution. Experts from the industry and academicians try to find the balanced solution among low-sulphur fuel, clean energy, and purposely fit scrubber by conventional statistical methods however failed to reach a satisfying conclusion. In addition, maritime datasets are usually massive, multi-source, and heterogeneous, it seems imperative for the maritime industry to adapt to the worldwide trend of intellectualisation and promote sustainable development. This work delineates and compares three main sulphur abatement solutions for ships through a thorough investigation of the current research state, and proposes a new framework based on a fusion model using modern big data and data mining algorithms. This work identifies and summarises major factors (with high impacts) in sulphur abatement solutions in the ocean shipping industry and integrate those high-level impacting factors to the proposed fusion model. The proposed framework can be optimised and utilised in determining suitable solutions for different ships, as well as shipping routes.

关键词： sulphur abatement maritime industry big data data mining clustering algorithm

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Novel methodology for developing a safety standard based on clustering of experts' assessments of safety requirements

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SAFETY SCIENCE 2021年 140卷

作者： Aly, Shady Tyrychtr, Jan Kvasnicka, Roman Vrana, Ivan Helwan Univ Fac Engn Helwan Cairo Egypt Czech Univ Life Sci Prague Fac Econ & Management Dept Informat Engn Prague Czech Republic Czech Univ Life Sci Prague Fac Econ & Management Dept Syst Engn Prague Czech Republic

The practice of developing product or system safety standards is currently conducted with high level of subjectivity, which affects the reliability of development outcomes. The decision to adopt specific safety requirements into the standard is usually based on the knowledge, intuition and experience of the technical expert involved in developing the body of the safety standard made up of those requirements or specifications. This is usually carried out without analyzing risk attributes or factors addressed by each safety requirement constituting a safety standard. This can lead to inaccuracy and lack of reliability due to either under-estimation or overestimation of risks involved. This research presents unprecedented, novel, structured and objective methodology, that aims to improve the practice of developing safety standards. In order to accomplish the above end, the proposed methodology acts to prioritize the system' safety requirements of each system's design elements based on a set of commonly identified risk factors they address. We have investigated the currently existing risk prioritization and multi-criterion decision making techniques, and found that machine learning data clustering approaches can be adequate especially in case of relatively large number of assessed decision alternatives (i.e., the assessed safety requirements). We have elaborated the merits of the proposed clustering-based methodology over existing prominent ones. The proposed methodology starts with analyzing product or system safety to identify common risk factors. A numerical scale is used to enable objective quantification of risk factors' values for each safety requirement by multiple experts. The obtained experts' numerical assessments of each safety requirement are then averaged to represent the raw data set containing risk profiles of each safety requirement assessed. This risk factors data set is then used by fuzzy c-means clustering algorithm, to organize them into groups of di

关键词： Safety standards development Safety requirements assessment and prioritization Risk assessment clustering algorithm Fuzzy c-means

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