Engineering Reports最新文献_第5页

Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning 利用深度学习自动驾驶控制无人机系统进行污水缺陷检测

Engineering Reports Pub Date : 2024-01-29 DOI: 10.1002/eng2.12852

B. Pandey, Digvijay Pandey, S. K. Sahani

{"title":"Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning","authors":"B. Pandey, Digvijay Pandey, S. K. Sahani","doi":"10.1002/eng2.12852","DOIUrl":"https://doi.org/10.1002/eng2.12852","url":null,"abstract":"This work proposes the use of an unmanned aerial vehicle (UAV) with an autopilot to identify the defects present in municipal sewerage pipes. The framework also includes an effective autopilot control mechanism that can direct the flight path of a UAV within a sewer line. Both of these breakthroughs have been addressed throughout this work. The UAV's camera proved useful throughout a sewage inspection, providing important contextual data that helped analyze the sewerage line's internal condition. A plethora of information useful for understanding the sewerage line's inner functioning and extracting interior visual details can be obtained from camera‐recorded sewerage imagery if a defect is present. In the case of sewerage inspections, nevertheless, the impact of a false negative is significantly higher than that of a false positive. One of the trickiest parts of the procedure is identifying defective sewerage pipelines and false negatives. In order to get rid of the false negative outcome or false positive outcome, a guided image filter (GIF) is implemented in this proposed method during the pre‐processing stage. Afterwards, the algorithms Gabor transform (GT) and stroke width transform (SWT) were used to obtain the features of the UAV‐captured surveillance image. The UAV camera's sewerage image is then classified as “defective” or “not defective” using the obtained features by a Weighted Naive Bayes Classifier (WNBC). Next, images of the sewerage lines captured by the UAV are analyzed using speed‐up robust features (SURF) and deep learning to identify different types of defects. As a result, the proposed methodology achieved more favorable outcomes than prior existing approaches in terms of the following metrics: mean PSNR (71.854), mean MSE (0.0618), mean RMSE (0.2485), mean SSIM (98.71%), mean accuracy (98.372), mean specificity (97.837%), mean precision (93.296%), mean recall (94.255%), mean F1‐score (93.773%), and mean processing time (35.43 min).","PeriodicalId":11735,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140488599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Preserving node similarity adversarial learning graph representation with graph neural network 利用图神经网络保存节点相似性对抗学习图表示法

Engineering Reports Pub Date : 2024-01-28 DOI: 10.1002/eng2.12854

Shangying Yang, Yinglong Zhang, Jiawei E, Xuewen Xia, Xing Xu

{"title":"Preserving node similarity adversarial learning graph representation with graph neural network","authors":"Shangying Yang, Yinglong Zhang, Jiawei E, Xuewen Xia, Xing Xu","doi":"10.1002/eng2.12854","DOIUrl":"https://doi.org/10.1002/eng2.12854","url":null,"abstract":"In recent years, graph neural networks (GNNs) have showcased a strong ability to learn graph representations and have been widely used in various practical applications. However, many currently proposed GNN‐based representation learning methods do not retain neighbor‐based node similarity well, and this structural information is crucial in many cases. To address this issue, drawing inspiration from generative adversarial networks (GANs), we propose PNS‐AGNN (i.e., Preserving Node Similarity Adversarial Graph Neural Networks), a novel framework for acquiring graph representations, which can preserve neighbor‐based node similarity of the original graph and efficiently extract the nonlinear structural features of the graph. Specifically, we propose a new positive sample allocation strategy based on a node similarity index, where the generator can generate vector representations that satisfy node similarity through adversarial training. In addition, we also adopt an improved GNN as the discriminator, which utilizes the original graph structure for recursive neighborhood aggregation to maintain the local structure and feature information of nodes, thereby enhancing the graph representation's ability. Finally, we experimentally demonstrate that PNS‐AGNN significantly improves various tasks, including link prediction, node classification, and visualization.","PeriodicalId":11735,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140490647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EPPTA: Efficient partially observable reinforcement learning agent for penetration testing applications EPPTA：用于渗透测试应用的高效部分可观察强化学习代理

Engineering Reports Pub Date : 2023-12-15 DOI: 10.1002/eng2.12818

Zegang Li, Qian Zhang, Guangwen Yang

{"title":"EPPTA: Efficient partially observable reinforcement learning agent for penetration testing applications","authors":"Zegang Li, Qian Zhang, Guangwen Yang","doi":"10.1002/eng2.12818","DOIUrl":"https://doi.org/10.1002/eng2.12818","url":null,"abstract":"In recent years, penetration testing (pen‐testing) has emerged as a crucial process for evaluating the security level of network infrastructures by simulating real‐world cyber‐attacks. Automating pen‐testing through reinforcement learning (RL) facilitates more frequent assessments, minimizes human effort, and enhances scalability. However, real‐world pen‐testing tasks often involve incomplete knowledge of the target network system. Effectively managing the intrinsic uncertainties via partially observable Markov decision processes (POMDPs) constitutes a persistent challenge within the realm of pen‐testing. Furthermore, RL agents are compelled to formulate intricate strategies to contend with the challenges posed by partially observable environments, thereby engendering augmented computational and temporal expenditures. To address these issues, this study introduces EPPTA (efficient POMDP‐driven penetration testing agent), an agent built on an asynchronous RL framework, designed for conducting pen‐testing tasks within partially observable environments. We incorporate an implicit belief module in EPPTA, grounded on the belief update formula of the traditional POMDP model, which represents the agent's probabilistic estimation of the current environment state. Furthermore, by integrating the algorithm with the high‐performance RL framework, sample factory, EPPTA significantly reduces convergence time compared to existing pen‐testing methods, resulting in an approximately 20‐fold acceleration. Empirical results across various pen‐testing scenarios validate EPPTA's superior task reward performance and enhanced scalability, providing substantial support for efficient and advanced evaluation of network infrastructure security.","PeriodicalId":11735,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A learning‐based approach to regression analysis for climate data–A case of Northeast China 基于学习的气候数据回归分析方法--以中国东北地区为例

Engineering Reports Pub Date : 2023-12-11 DOI: 10.1002/eng2.12797

Jiaxu Guo, Yidan Xu, Liang Hu, Xianwei Wu, Gaochao Xu, Xilong Che

{"title":"A learning‐based approach to regression analysis for climate data–A case of Northeast China","authors":"Jiaxu Guo, Yidan Xu, Liang Hu, Xianwei Wu, Gaochao Xu, Xilong Che","doi":"10.1002/eng2.12797","DOIUrl":"https://doi.org/10.1002/eng2.12797","url":null,"abstract":"Global climate change is an important issue that all of humanity needs to address together. Precipitation is an important climatic feature for agricultural development and food security, and the study of precipitation and its associated climatic factors is important for the analysis of global change. As an important part of China's food production, Northeast China has a temperate monsoon climate with simultaneous rain and heat, which is favorable for crop growth. In this paper, a scientific workflow for climate data analysis with a learning‐based method is designed. Using climate data from typical models in CMIP6, a machine learning‐based approach is used to establish regression relationships between precipitation and climate variables such as temperature, humidity and wind speed in Northeast China, which is validated through a time series approach. We design a weight‐based model ensemble method and a learning‐based bias correction method, so that the ensemble model can achieve better performance. We also analyze the precipitation trends in Northeast China under the three Shared Socio‐economic Pathways (SSPs). This will help researchers to analyze the long‐term evolution and factors of climate.","PeriodicalId":11735,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138981265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Refactoring BZIP2 on the new‐generation sunway supercomputer 在新一代神威超级计算机上重构BZIP2

Engineering Reports Pub Date : 2023-11-03 DOI: 10.1002/eng2.12806

Xiaohui Liu, Zekun Yin, Haodong Tian, Wubing Wan, Mengyuan Hua, Wenlai Zhao, Zhenchun Huang, Ping Gao, Fangjin Zhu, Hua Wang, Xiaohui Duan

{"title":"Refactoring BZIP2 on the new‐generation sunway supercomputer","authors":"Xiaohui Liu, Zekun Yin, Haodong Tian, Wubing Wan, Mengyuan Hua, Wenlai Zhao, Zhenchun Huang, Ping Gao, Fangjin Zhu, Hua Wang, Xiaohui Duan","doi":"10.1002/eng2.12806","DOIUrl":"https://doi.org/10.1002/eng2.12806","url":null,"abstract":"High‐performance computing is progressively assuming a fundamental role in advancing scientific research and engineering domains. However, the ever‐expanding scales of scientific simulations pose challenges for efficient data I/O and storage. The data compression technology has garnered significant attention as a solution to reduce data transmission and storage costs while enhancing performance. In particular, the BZIP2 lossless compression algorithm has been widely used due to its exceptional compression ratio, moderate compression speed, high reliability, and open‐source nature. This paper focuses on the design and realization of a parallelized BZIP2 algorithm tailored for deployment on the New‐Generation Sunway supercomputing platform. By leveraging the unique cache patterns of the New‐Generation Sunway processor, we propose the highly tuned multi‐threading and multi‐node implementations of the BZIP2 applications for different scenarios. Moreover, we also propose the efficient BZIP2 libraries based on the management processing element and computing processing element which support the commonly used high‐level (de)compression interfaces. The test results indicate that the our multi‐threading implementation achieves maximum speedup of 23.09 (8.57) in decompression(compression) compared to the sequential implementation. Furthermore, the multi‐node implementation achieves 50.81% (26.35%) parallel efficiency and peak performance of 16.6 GB/s (52.8 GB/s) for compression(decompression) when scaling up to 2048 processes.","PeriodicalId":11735,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135821318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accelerating ray tracing engine of BLENDER on the new Sunway architecture 在新的Sunway架构上加速BLENDER的光线追踪引擎

Engineering Reports Pub Date : 2023-10-31 DOI: 10.1002/eng2.12789

Zhaoqi Sun, Zhen Wang, Mengyuan Hua, Puyu Xiong, Wubing Wan, Ping Gao, Wenlai Zhao, Zhenchun Huang, Lin Han

{"title":"Accelerating ray tracing engine of <scp>BLENDER</scp> on the new Sunway architecture","authors":"Zhaoqi Sun, Zhen Wang, Mengyuan Hua, Puyu Xiong, Wubing Wan, Ping Gao, Wenlai Zhao, Zhenchun Huang, Lin Han","doi":"10.1002/eng2.12789","DOIUrl":"https://doi.org/10.1002/eng2.12789","url":null,"abstract":"Abstract With the increasing popularity of high‐resolution displays, there is a growing demand for more realistic rendered images. Ray tracing has become the most effective algorithm for image rendering, but its complexity and large amount of computing data require sophisticated HPC solutions. In this article, we present our efforts to port the ray tracing engine CYCLES of Blender to the new generation of Sunway supercomputers. We propose optimizations that are tailored to the new hardware architecture, including a multi‐level parallel scheme that efficiently maps and scales Blender onto the novel Sunway architecture, strategies to address memory bottlenecks, a revised task dispatching method that achieves excellent load balancing, and a pipeline approach that maximizes computation and communication overlap. By combining all these optimizations, we achieve a significant reduction in rendering time for a single‐frame image, from 2260 s using the single‐core serial version to 71 s using 48 processes, which is a speedup of about 128×. Accelerating the ray tracing engine CYCLES of Blender in the new generation of Sunway supercomputers.","PeriodicalId":11735,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long‐term performance of single‐lap joints: Review, challenges and prospects in civil engineering 单搭接节点的长期性能:土木工程的回顾、挑战与展望

Engineering Reports Pub Date : 2023-09-19 DOI: 10.1002/eng2.12769

Yue Shu, Xuhong Qiang, Xu Jiang, Yi Xiao, Hao Dong

{"title":"Long‐term performance of single‐lap joints: Review, challenges and prospects in civil engineering","authors":"Yue Shu, Xuhong Qiang, Xu Jiang, Yi Xiao, Hao Dong","doi":"10.1002/eng2.12769","DOIUrl":"https://doi.org/10.1002/eng2.12769","url":null,"abstract":"Abstract Compared with traditional technology, bonding technology is more suitable for civil structure reinforcement because of its cost‐efficiency and superior mechanical properties. However, research on the long‐term performance of single‐lap joints (SLJs) requires better organization and comprehension. This article aims to investigate the long‐term performance and optimization design of SLJs. The main factors influencing the long‐term performance of SLJs from both material and component levels are discussed. The moisture diffusion mechanisms of bulk adhesives and the degradation mechanisms of SLJs are explored. Moreover, the optimization design of SLJs focuses on evaluating the overlap length, adhesive layer thicknesses, and changes in adhesives along the overlap length based on available literature. It is found that the applicability of diffusion models should be validated, and the selection of the models should consider working environments and types of adhesives. Exploring failure mechanisms and design criteria for the mixed SLJs in hygrothermal environments with/without sustained or alternating load is significant for the optimization design. This article indicates the limitations on the shear strength and long‐term performance of SLJs in available studies and provides insights into the challenges and prospects of their optimization design.","PeriodicalId":11735,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135011417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Field‐programmable gate array acceleration of the Tersoff potential in LAMMPS LAMMPS中Tersoff电位的现场可编程门阵列加速

Engineering Reports Pub Date : 2023-05-29 DOI: 10.1002/eng2.12694

Quan Deng, Qiang Liu

{"title":"Field‐programmable gate array acceleration of the Tersoff potential in LAMMPS","authors":"Quan Deng, Qiang Liu","doi":"10.1002/eng2.12694","DOIUrl":"https://doi.org/10.1002/eng2.12694","url":null,"abstract":"Abstract Molecular dynamics simulation is a common method to help humans understand the microscopic world. The traditional general‐purpose high‐performance computing platforms are hindered by low computational and power efficiency, constraining the practical application of large‐scale and long‐time many‐body molecular dynamics simulations. In order to address these problems, a novel molecular dynamics accelerator for the Tersoff potential is designed based on field‐programmable gate array (FPGA) platforms, which enables the acceleration of LAMMPS using FPGAs. Firstly, an on‐the‐fly method is proposed to build neighbor lists and reduce storage usage. Besides, multilevel parallelizations are implemented to enable the accelerator to be flexibly deployed on FPGAs of different scales and achieve good performance. Finally, mathematical models of the accelerator are built, and a method for using the models to determine the optimal‐performance parameters is proposed. Experimental results show that, when tested on the Xilinx Alveo U200, the proposed accelerator achieves a performance of 9.51 ns/day for the Tersoff simulation in a 55,296‐atom system, which is a 2.00 increase in performance when compared to Intel I7‐8700K and 1.70 to NVIDIA Tesla K40c under the same test case. In addition, in terms of computational efficiency and power efficiency, the proposed accelerator achieves improvements of 2.00 and 7.19 compared to Intel I7‐8700K, and 4.33 and 2.11 compared to NVIDIA Titan Xp, respectively.","PeriodicalId":11735,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135792476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0