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Sustainable remediation of failed slope using helical soil nails

Journal of Mountain Science 2023年第6期20卷 1742-1758页

作者： Saurabh RAWAT Ashok Kumar GUPTA Pankaj SHARMA Department of Civil Engineering Jaypee University of Information Technology Department of Civil Engineering Chitkara University Institute of Engineering and TechnologyChitkara University

The performance of a helical soil nailed structure is dependent on the installation torque required and the consequent pullout resistance *** present research work aims at proposing theoretical models to estimate the required torque during installation of helical soil ***,theoretical models are also developed to predict the pullout capacity of single and group of the helical nail for uniform and staggered *** proposed model predicts the pure-elastic and elastic-plastic pullout behavior of different helical *** equation for estimating the capacity-totorque Ratio(Kt)has also been developed for different nail shaft *** results from the proposed models are validated with experimental results obtained from model testing of both single and group of helical *** predicted results are also compared for validation with the published *** results for installation torque and pullout load depict that the developed models predict values which are in accordance with the experimental results and are also found in good agreement with the published ***,the proposed models can effectively be used by the filed engineers for estimating the required installation torque and corresponding pullout capacities for single or double plate helical soil nails in cohesionless soil under surcharge pressure range of 0–50k Pa.

关键词： Theoretical torque Pullout force Pure elastic Elastic plastic Capacity to torque ratio

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Optimal Multi-Bitrate Video Caching and Processing in Edge Computing: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年第13期12卷 25059-25076页

作者： Guo, Min Zhang, Di Xing, Weiwei Shao, Xun Liu, Zhi Zhang, Yaoxue Beijing Jiaotong University School of Software Engineering Beijing100044 China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi441-8580 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan Tsinghua University Department of Computer Science and Technology Beijing100084 China

By caching and transcoding video files on edge servers, video edge caching (VEC) can alleviate network congestion and improve user experience. To achieve this, VEC needs to address resource allocation and pricing problems and develop caching and transcoding strategies. However, existing works have not effectively coordinated and resolved these issues. Towards this end, we propose a game-theoretic multi-bitrate video caching and processing method for VEC. Specifically, to address these interdependent issues concurrently, we first incorporate multidimensional strategies into the system model and formalize the profit maximization problems of the network service provider and multiple video providers (VPs) as a constrained multi-objective optimization problem. Second, we transform the problem into a Stackelberg game and identify a Nash equilibrium as the solution. To solve the game problem, we decompose the game process into two stages focusing on resource competition and price determination, respectively. Then, we address the problems of transcoding and caching strategies development, and resource allocation in parallel across varying resource price conditions. The transcoding strategy is determined based on the constraints imposed by delay, value, and the source video. For the caching strategy development and resource allocation, we transform them into two grouped knapsack problems and apply the "dominant strategy» from game theory to identify the "feasible strategy» of VPs. Finally, we determine the resource prices in a traversal manner. Theoretical analysis shows that our proposed method achieves the optimal solution. Experimental results show that our algorithm achieve a 6.04% increase in overall profit compared to the greedy strategy algorithm. © 2025 IEEE.

关键词： Costs

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A Framework for Industrial Inspection System using Deep Learning

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Annals of Data Science 2024年第2期11卷 445-478页

作者： Hridoy, Monowar Wadud Rahman, Mohammad Mizanur Sakib, Saadman Department of Mechatronics & amp Industrial Engineering Chittagong University of Engineering & ampTechnology Chattogram Bangladesh Department of Mechanical Engineering Chittagong University of Engineering & ampTechnology Chattogram Bangladesh Department of Computer Science & amp Engineering Chittagong University of Engineering & ampTechnology Chattogram Bangladesh

Industrial Inspection systems are an essential part of Industry 4.0. An automated inspection system can significantly improve product quality and reduce human labor while making their life easier. However, a deep learning-based camera inspection system requires a large amount of data to classify the defective products accurately. In this paper, a framework is proposed for an industrial inspection system with the help of deep learning. Additionally, A new dataset of hex-nut products is proposed containing 4000 images, i.e., 2000 defective and 2000 non-defective. Moreover, different CNN architectures, i.e., Custom CNN, Inception ResNet v2, Xception, ResNet 101 v2, ResNet 152 v2, are experimented with the concept of transfer learning on the new hex-nut dataset. Fine-tuning the CNN architectures is performed by freezing the last 14 layers, which provided the optimal architecture, i.e., Xception (last 14 layers trainable, excluding the fully connected layer). The proposed framework can efficiently separate the defective products from the non-defective products with 100% accuracy on the hex nut dataset. Furthermore, the proposed optimal Xception architecture has experimented on a publicly available casting material dataset which produced 99.72% accuracy, outperforming existing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Industry 4.0

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Revolutionizing Image Captioning: Integrating Attention Mechanisms with Adaptive Fusion Gates

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2024年第3期51卷 212-221页

作者： Sheng, Shou-Jun Zhou, Zi-Wei School of Computer and Software Engineering University of Science and Technology Liaoning AnShan114051 China

In order to dynamically create a sequence of textual descriptions for images, image description models often make use of the attention mechanism, which involves an automatic focus on different regions within an image. However, a prevalent issue with current attention mechanisms is their tendency to overlook essential elements within the image, prioritizing contextual aspects of the object when generating descriptive text. This constraint results in a decrease in the precision of the textual descriptions produced. To address this issue and improve the accuracy of image interpretation, a proposed model for image description utilizes an attention-based approach and includes a multi-layer decoder and a fusion gate. This model is based on an encoder-decoder architecture and utilizes Residual Network (ResNet) framework for feature extraction during the encoding phase, thereby extending the encoder-decoder structure into the decoding phase. Within this framework, an adaptive fusion gate mechanism is introduced and combined with multi-layer cascade decoders to facilitate the generation of utterances. This allows decoders from lower layers to actively contribute to the final text prediction phase, thereby incrementally improving the accuracy of the predicted text and generating more precise descriptions. MS COCO 2014 dataset has been utilized for the purpose of training and validating the effectiveness of this model in understanding images. The results clearly and unequivocally establish the model’s capacity to generate exceptional predictions. When compared with the top-performing models, it has demonstrated significant enhancements, as indicated by a 0.096 rise in BLEU_1 metric, a 0.153 improvement in ROUGE_L metric, and a remarkable 0.32 increase in CIDEr metric on MS COCO dataset. The overall improvement in performance across all evaluation criteria highlights the model’s alignment with the requirements of image understanding applications. © (2024), (International Associ

关键词： Computer vision

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YOLOv7-DSE: An Efficient Safety Equipment Detection Network

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2024年第6期51卷 572-581页

作者： Ren, Jiaxin Cui, Wenhua Tao, Ye Shi, Tianwei School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Safety equipment detection is an important application of object detection, receiving widespread attention in fields such as smart construction sites and video surveillance. Significant progress has been made in object detection due to the rapid development of deep learning. Multi-scale targets and complex scenes increase the likelihood of false positives and missed detections, which can affect the accuracy of the detection. To address this issue, this study proposes YOLOv7-DSE. It is a small complex target scene detection network based on the improved YOLOv7. Also, we have created a private dataset of safety equipment. First, we enhanced the ELAN and MP backbone networks. Backbone is replaced by ordinary convolution by the depthwise separable convolution. We enabled the backbone network to extract deeper image features without increasing the amount of parameters and computation. Simultaneously, the model incorporates the EIOU loss function to improve its convergence speed and positioning effect. Secondly, we propose a new ELAN-SPD structure in the head network. Based on the ELAN structure, a space-to-depth convolutional layer is added to fully downsample the feature map, preserving all learnable features. Our network model can better detect objects with significant size differences faced with complex scenes. YOLOv7-DSE achieved the mAP of 82.38%, surpassing the original YOLOv7 with 2.64%. The YOLOv7-DSE model has a minor size compared to the baseline model. Our improvement has reduced the model parameters by 22.4%. © (2024), (International Association of Engineers). All rights reserved.

关键词： Object detection

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Mechanical performance of cold mix asphalt containing cup lump rubber as a sustainable bio-modifier

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Journal of Traffic and Transportation engineering(English Edition) 2024年第3期11卷 424-440页

作者： Syed Abdul Ghafar Muhammad Naqiuddin Mohd Warid Norhidayah Abdul Hassan Zulfiqar Ali Jattak Ashraf Abdalla Mohammed Radwan Faculty of Civil Engineering Universiti Teknologi MalaysiaSkudai 81310Malaysia Department of Civil Engineering Balochistan University of Information TechnologyEngineering&Management SciencesQuettaPakistan Department of Civil Engineering Balochistan University of Engineering and TechnologyKhuzdarPakistan Department of Civil Engineering Faculty of EngineeringUniversity of TripoliTripoliLibya

The road construction industry aims to contribute to the protection of already compromised *** mix asphalt(CMA)is a measure initiated by the road industry to protect the environment and preserve *** having additional benefits,CMA has attracted little attention due to its inferior ***'s performance is enhanced using a sustainable binder bio-modifier,natural cup lump rubber(CLR)is one of *** study evaluated the tensile properties,rutting,moisture susceptibility,and adhesion properties of CLR-modified CMA(CMA-CR).The tensile property was enhanced by 26%due to CLR ***-CR had excellent rutting resistance of less than 2 mm rut depth at 10,000 load cycles,showing 70%improvement compared with conventional *** susceptibility evaluation indicated that CMA-CR had tensile strength ratio(TSR)value of 104%,satisfying the minimum 80%requirement of AASHTO *** also retained more than 96%bitumen *** moisture damage resistance was improved by 12%and 10%in terms of TSR and stripping,*** durability results revealed that the CMA-CR mixture prevented higher mass loss,representing 14%improvement compared with conventional CMA.

关键词： Cup lump rubber Cup lump modified cold mix asphalt Moisture damage Tensile property Rutting Digital imaging

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Simulation and Experiment of Windage Power Loss of A Shrouded Spiral Bevel Gears under Oil Injection Lubrication

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Chinese Journal of Mechanical engineering 2025年第2期38卷 413-432页

作者： Bo Li Kai Rong Xuyang Zhang Sanmin Wang U-Xuan Tan Xuanyuan Su Laifa Tao School of Mechanical Engineering Northwestern Polytechnical University Department of Engineering Product Development Singapore University of Technology and Design Hangzhou International Innovation Institute Beihang University National Key Laboratory of Science and Technology on Reliability and Environmental Engineering School of Reliability and Systems Engineering Beihang University

During high-speed rotation, the surface of aeronautic spiral bevel gears will generate significant pressure and viscous forces, which will cause a certain amount of windage power loss and reduce the efficiency of the transmission system. Based on the computational fluid dynamics, this paper analyzes the windage power loss of a single spiral bevel gear and a spiral bevel gear pair under oil injection lubrication. In addition, the shroud is used to suppress gear windage loss, and the clearance size and opening angle of the designed shroud are optimized. Finally, by comparing and analyzing the experimental results, the following conclusions were obtained:(1) For a single gear, the speed is the most important factor affecting windage loss, followed by the hand of spiral, and rotation direction;(2) For gear pairs, under oil injection lubrication, the input speed has the greatest impact on windage power loss, followed by the influence of oil injection port speed, temperature and oil injection port pressure;(3) Installing a shroud is an effective method to reduce windage power loss;(4) In the pure air phase, the smaller the clearance between the shroud and the gear surface, and the smaller the radial direction between the shroud and the shaft, the better the effect of reducing windage;(5) In the two-phase flow of oil and gas, it is necessary to design oil drainage holes on the shroud to ensure the smooth discharge of lubricating oil and improve the drag reduction effect.

关键词： Computational fluid dynamics (CFD) Oil injection lubrication Shroud Spiral bevel gear Windage power loss

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Prediction of bearing capacity of pile foundation using deep learning approaches

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Frontiers of Structural and Civil engineering 2024年第6期18卷 870-886页

作者： Manish KUMAR Divesh Ranjan KUMAR Jitendra KHATTI Pijush SAMUI Kamaldeep Singh GROVER Department of Civil Engineering SRM Institute of Science and Technology Tiruchirappalli CampusTrichy 621105India Department of Civil Engineering National Institute of Technology PatnaBihar 800005India Department of Civil Engineering Faculty of EngineeringThammasat School of EngineeringThammasat UniversityBangkok10200Thailand Department of Civil Engineering Rajasthan Technical UniversityKota 324010India

The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile *** research compares the Deep Neural Networks(DNN),Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),Long Short-Term Memory(LSTM),and Bidirectional LSTM(BiLSTM)algorithms utilizing a data set of 257 dynamic pile load tests for the first ***,this research illustrates the multicollinearity effect on DNN,CNN,RNN,LSTM,and BiLSTM models’performance and accuracy for the first time.A comprehensive comparative analysis is conducted,employing various statistical performance parameters,rank analysis,and error matrix to evaluate the performance of these *** performance is further validated using external validation,and visual interpretation is provided using the regression error characteristics(REC)curve and Taylor *** from the comparative analysis reveal that the DNN(Coefficient of determination(R^(2))_(training(TR))=0.97,root mean squared error(RMSE)_(TR)=0.0413;R^(2)_(testing(TS))=0.9,RMSE_(TS)=0.08)followed by BiLSTM(R^(2)_(TR)=0.91,RMSE_(TR)=0.782;R^(2)_(TS)=0.89,RMSE_(TS)=0.0862)model demonstrates the highest performance *** is noted that the BiLSTM model is better than LSTM because the BiLSTM model,which increases the amount of information for the network,is a sequence processing model made up of two LSTMs,one of which takes the input in a forward manner,and the other in a backward *** prediction of pile-bearing capacity is strongly influenced by ram weight(having a considerable multicollinearity level),and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network *** this study,the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.

关键词： deep learning algorithms high-strain dynamic pile test bearing capacity of the pile

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Federated learning and non-federated learning based power forecasting of photovoltaic/wind power energy systems:A systematic review

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Energy and AI 2024年第4期18卷 389-409页

作者： Ferial ElRobrini Syed Muhammad Salman Bukhari Muhammad Hamza Zafar Nedaa Al-Tawalbeh Naureen Akhtar Filippo Sanfilippo Department of Renewable Energies Saad Dahlab UniversityBlida09000Algeria Department of Electrical Engineering Capital University of Science and TechnologyIslamabad44000Pakistan Department of Engineering Sciences University of AgderGrimstad4879Norway Department of Renewable Energy Engineering Al al-Bayt University25113 MafraqJordan Department of Software Engineering Kaunas University of Technology44029 KaunasLithuania

Renewable energy sources,particularly photovoltaic and wind power,are essential in meeting global energy mands de-while minimising environmental *** photovoltaic(PV)and wind power(WP)forecasting is crucial for effective grid management and sustainable energy ***,traditional forecasting methods encounter challenges such as data privacy,centralised processing,and data sharing,particularly with dispersed data *** review paper thoroughly examines the necessity of forecasting models,methodologies,and data integrity,with a keen eye on the evolving landscape of Federated Learning(FL)in PV and WP *** with an introduction highlighting the significance of forecasting models in optimising renewable energy resource utilisation,the paper delves into various forecasting techniques and emphasises the critical need for data integrity and security.A comprehensive overview of non-Federated Learning-based PV and WP forecasting is presented based on high-quality journals,followed by in-depth discussions on specific non-Federated Learning approaches for each power *** paper subsequently introduces FL and its variants,including Horizontal,Vertical,Transfer,Cross-Device,and Cross-Silo FL,highlighting the crucial role of encryption mechanisms and addressing associated ***,drawing on extensive investigations of numerous pertinent articles,the paper outlines the innovative horizon of FL-based PV and wind power forecasting,offering insights into FL-based methodologies and concluding with observations drawn from this *** review synthesises critical knowledge about PV and WP forecasting,leveraging the emerging paradigm of ***,this work contributes to the advancement of renewable energy integration and the optimisation of power grid management sustainably and securely.

关键词： Privacy-preserving Federated learning Transfer learning PV power forecasting Wind power forecasting Deep learning

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Privacy-preserving recommendation with coarse-grained spatiotemporal contexts

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Science China(Information Sciences) 2025年第4期68卷 66-81页

作者： Lei CHEN Chen GAO Jiahuan LEI Xiaoyi DU Xinlei SHI Hengliang LUO Depeng JIN Yong LI Meng WANG Department of Electronic Engineering BNRist Tsinghua University Meituan Inc. School of Computer Science and Information Engineering Hefei University of Technology

The behavior of users on online life service platforms like Meituan and Yelp often occurs within specific finegrained spatiotemporal contexts(i.e., when and where). Recommender systems, designed to serve millions of users, typically operate in a fully server-based manner, requiring on-device users to upload their behavioral data, including fine-grained spatiotemporal contexts, to the server, which has sparked public concern regarding privacy. Consequently, user devices only upload coarse-grained spatiotemporal contexts for user privacy protection. However, previous research mostly focuses on modeling fine-grained spatiotemporal contexts using knowledge graph convolutional models, which are not applicable to coarse-grained spatiotemporal contexts in privacy-constrained recommender systems. In this paper, we investigate privacy-preserving recommendation by leveraging coarse-grained spatiotemporal contexts. We propose the coarse-grained spatiotemporal knowledge graph for privacy-preserving recommendation(CSKG), which explicitly models spatiotemporal co-occurrences using common-sense knowledge from coarse-grained contexts. Specifically, we begin by constructing a spatiotemporal knowledge graph tailored to coarse-grained spatiotemporal contexts. Then we employ a learnable metagraph network that integrates common-sense information to filter and extract co-occurrences. CSKG evaluates the impact of coarsegrained spatiotemporal contexts on user behavior through the use of a knowledge graph convolutional network. Finally, we introduce joint learning to effectively learn representations. By conducting experiments on two real large-scale datasets,we achieve an average improvement of about 11.0% on two ranking metrics. The results clearly demonstrate that CSKG outperforms state-of-the-art baselines.

关键词： privacy-preserveing coarse-grained spatiotemporal contexts recommender systems

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