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2nd International Conference on geotechnical Issues in Energy, Infrastructure and Disaster Management, ICGEID 2024

作者： Kainthola, Ashutosh Pandey, Vishnu Himanshu Ratnam Singh, T.N. Geo-Engineering and Computing Laboratory Department of Geology Banaras Hindu University Uttar Pradesh Varanasi India Department of Civil and Environmental Engineering IIT Patna Bihar Patna India

ISBN: (纸本)9789819717569

Present research details the comparison of predictive efficiency of linear regression, gradient boosting regression, and slime mould algorithm optimised gradient boosting regression. These algorithms have been used to predict the factor of safety of cut slopes in lower Tons valley, Uttarakhand, India. Initially, 103 soil slopes were examined for their stability factor in finite difference code. Four parameters like cohesion, angle of internal friction, slope height, and slope angle were selected to develop the stability (factor of safety) prediction models. Based on the statistical accuracy indices like R2 and mean absolute error, the hyper-parameter optimized model based on slime mould algorithm (i.e., SMA-GBR) had the best prediction capability. And, the R2 for SMA-GBR model is 0.92 and mean absolute error is 0.063. The next better performer is linear regression model with value of 0.84 and 0.094 for R2 and mean absolute error respectively. And, the gradient boosting algorithm model has the least predictive capacity as compared to other two prediction models developed in the present work. However, its capability is significantly increased as its hyper-parameter are fine-tuned through slime mould algorithms (SMA). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction

arXiv

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arXiv 2025年

作者： Liu, Wenxuan Li, Zixuan Bai, Long Zuo, Yuxin Xu, Daozhu Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Geo-Information Engineering Shaanxi Xi’an China

Developing a general-purpose extraction system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the challenge comes from two aspects: 1) The absence of an efficient and effective annotation method. 2) The absence of a powerful extraction method can handle massive types. For the first challenge, we propose a collaborative annotation method based on Large Language Models (LLMs). Through collaboration among multiple LLMs, it first refines annotations of trigger words from distant supervision and then carries out argument annotation. Next, a voting phase consolidates the annotation preferences across different LLMs. Finally, we create the EEMT dataset, the largest EE dataset to date, featuring over 200,000 samples, 3,465 event types, and 6,297 role types. For the second challenge, we propose an LLM-based Partitioning EE method called LLM-PEE. To overcome the limited context length of LLMs, LLM-PEE first recalls candidate event types and then splits them into multiple partitions for LLMs to extract events. The results in the supervised setting show that LLM-PEE outperforms the state-of-the-art methods by 5.4% in event detection and 6.1% in argument extraction. In the zero-shot setting, LLM-PEE achieves up to 12.9% improvement compared to mainstream LLMs, demonstrating its strong generalization capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space 26

Safe Reinforcement Learning for Social Motor Coordination in...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing HoustonTX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints. © 2025 IEEE.

关键词： Reinforcement learning

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PFS3F: Probabilistic Fusion of Superpixel-wise and Semantic-aware Structural Features for Hyperspectral Image Classification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhang, Ying Duan, Puhong Liang, Lianhui Kang, Xudong Li, Jun Plaza, Antonio Hunan University of Technology College of Railway Transportation Zhuzhou412007 China Hunan University School of Robotics Changsha410082 China Guangxi University School of Electrical Engineering Nanning530004 China China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing the School of Computer Science Wuhan430078 China Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica University of Extremadura CaceresE-10071 Spain

Processing high-dimensional data cubes and developing high-performance classifiers are core objectives in the field of hyperspectral image classification (HSIC). Superpixel-based methods are widely used in HSIC due to their efficacy in reducing redundant information and enhancing local features. However, imprecise segmentation, especially in complex structures and textures of hyperspectral images (HSIs), may lead to inconsistencies in the regions extracted by superpixels and the boundaries between different ground objects. Such inconsistencies significantly degrade the classification performance of HSIs. Alternatively, when parameter settings are inaccurate, edge-aware feature extraction methods often introduce sharpening artifacts at the image boundaries, resulting in a decrease in classification accuracy. To effectively address these challenges, we propose a novel probabilistic fusion method for HSIC. This method consists of the following stages. First, spatial information is extracted by a multiscale superpixel segmentation method and then probabilistically optimized by the extended random walk (ERW) method. Next, semantic-aware structural features (S2Fs) are extracted along with edge information of different objects. Lastly, a probabilistic framework is proposed to fuse the class probabilities of superpixel-based spatial information and semantic-aware structural features. Experimental results on three real datasets show state-of-the-art classification performance, even with limited training sets. ©1991-2012 IEEE.

关键词： Superpixels

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space

Safe Reinforcement Learning for Social Motor Coordination in...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Fan Yang Yuening Huang Chao Zhai Hehong Zhang China University of Geosciences Wuhan Campus School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing Houston Texas United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

关键词： Autism Filters Reinforcement learning Games Variable speed drives Motors Trajectory Safety Mirrors Motion control

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Spatio-temporal fusion for remote sensing data:an overview and new benchmark

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Science China(Information Sciences) 2020年第4期63卷 7-23页

作者： Jun LI Yunfei LI Lin HE Jin CHEN Antonio PLAZA Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University School of Automation Science and Engineering South China University of Technology State Key Laboratory of Earth Surface Processes and Resource Ecology Institute of Remote Sensing Science and Engineering Faculty of Geographical ScienceBeijing Normal University Hyperspectral Computing Laboratory Department of Technology of Computers and CommunicationsEscuela Politécnica University of Extremadura

Spatio-temporal fusion(STF) aims at fusing(temporally dense) coarse resolution images and(temporally sparse) fine resolution images to generate image series with adequate temporal and spatial resolution. In the last decade, STF has drawn a lot of attention and many STF methods have been ***, to date the STF domain still lacks benchmark datasets, which is a pressing issue that needs to be addressed in order to foster the development of this field. In this review, we provide(for the first time in the literature) a robust benchmark STF dataset that includes three important characteristics:(1) diversity of regions,(2) long timespan, and(3) challenging scenarios. We also provide a survey of highly representative STF techniques, along with a detailed quantitative and qualitative comparison of their performance with our newly presented benchmark dataset. The proposed dataset is public and available online.

关键词： spatio-temporal fusion(STF) multi-temporal remote sensing data benchmark data experimental validation

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A new sensor bias-driven spatio-temporal fusion model based on convolutional neural networks

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Science China(Information Sciences) 2020年第4期63卷 24-39页

作者： Yunfei LI Jun LI Lin HE Jin CHEN Antonio PLAZA Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University School of Automation Science and Engineering South China University of Technology State Key Laboratory of Earth Surface Processes and Resource Ecology Institute of Remote Sensing Science and Engineering Faculty of Geographical ScienceBeijing Normal University Hyperspectral Computing Laboratory Department of Technology of Computers and CommunicationsEscuela Politécnica University of Extremadura

Owing to the tradeoff between scanning swath and pixel size, currently no satellite Earth observation sensors are able to collect images with high spatial and temporal resolution simultaneously. This limits the application of satellite images in many fields, including the characterization of crop yields or the detailed investigation of human-nature interactions. Spatio-temporal fusion(STF) is a widely used approach to solve the aforementioned problem. Traditional STF methods reconstruct fine-resolution images under the assumption that changes are able to be transferred directly from one sensor to another. However, this assumption may not hold in real scenarios, owing to the different capacity of available sensors to characterize changes. In this paper, we model such differences as a bias, and introduce a new sensor bias-driven STF model(called Bia STF) to mitigate the differences between the spectral and spatial distortions presented in traditional methods. In addition, we propose a new learning method based on convolutional neural networks(CNNs) to efficiently obtain this bias. An experimental evaluation on two public datasets suggests that our newly developed method achieves excellent performance when compared to other available approaches.

关键词： spatio-temporal fusion(STF) convolutional neural networks(CNNs) sensor bias-driven STF

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Wishart Deeplab Network for Polarimetric SAR Image Classification 3rd

Wishart Deeplab Network for Polarimetric SAR Image Classific...

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3rd China Intelligent Robotics Annual Conference, CCF CIRAC 2022

作者： Shi, Junfei He, Tiansheng Jin, Haiyan Wang, Hua Xu, Weinan State Key Laboratory of Geo-Information Engineering Shaanxi Xi’an710048 China School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Shaanxi Xi’an710048 China

ISBN: (纸本)9789819903009

Polarimetric Synthetic Aperture Radar (PolSAR) images have attracted much attention with abundant polarimetric scattering information. In recent years, many deep learning models have been proposed and highly expanded to PolSAR image classification. However, how to learn the complex matrix information is an indispensable problem. To address this problem, a Wishart Deeplab network is developed, which can not only learn the polarimetric matrix structure by designing Wishart network level but also learn multi-scale polarimetric scattering features by incorporating dilated convolution. Specifically, the Wishart-Deeplab based PolSAR classification model is constructed by designing the Wishart convolution operation to learn the statistical information of PolSAR data. Then, a deeplabV3 + network is followed to obtain the multi-scale semantic features by dilated convolution. By this way, statistical distrubtion-based and high-level semantic features are adaptively learned to improve the performance of the network. The experiments are conducted on two sets of real PolSAR data and results show that this method can obtain better classification results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex engineering Problems: Principles and Applications

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2023-2027页

作者： Danial Jahed Armaghani Ahmed SalihMohammed Ramesh Murlidhar Bhatawdekar Pouyan Fakharian Ashutosh Kainthola Wael Imad Mahmood School of Civil and Environmental Engineering University of Technology SydneyUltimoNSW 2007Australia College of Engineering University of SulaimaniKurdistan46001Iraq Engineering Department American University of IraqSulaimaniKurdistan46001Iraq Centre of Tropical Geoengineering(GEOTROPIK) Institute of Smart Infrastructure and Innovative Engineering(ISIIC)Faculty of Civil EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Faculty of Civil Engineering Semnan UniversitySemnanIran Geo-Engineering and Computing Laboratory Banaras Hindu UniversityVaranasi221005India Scientific Research Centre Soran UniversitySoranErbilKurdistan-Region44008Iraq

Computational Intelligent(CI)systems represent a pivotal intersection of cutting-edge technologies and complex engineering challenges aimed at solving real-world *** comprehensive body of work delves into the realm of CI,which is designed to tackle intricate and multifaceted engineering problems through advanced computational *** history of CI systems is a fascinating journey that spans several decades and has its roots in the development of artificial intelligence and machine learning *** a wide array of practical examples and case studies,this special issue bridges the gap between theoretical concepts and practical implementation,shedding light on how CI systems can optimize processes,design solutions,and inform decisions in complex engineering *** compilation stands as an essential resource for both novice learners and seasoned practitioners,offering a holistic perspective on the potential of CI in reshaping the future of engineering problem-solving.

关键词： offering bridges landscape

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Accelerating Ordinary Kriging Interpolation Algorithm on GPUs

Accelerating Ordinary Kriging Interpolation Algorithm on GPU...

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2022 IEEE International Conference on Advances in Electrical engineering and Computer Applications, AEECA 2022

作者： Liu, Song Zhao, Chenyu An, Xiaoya Gao, Kai Ji, Yingjie Wu, Weiguo State Key Laboratory of Geo-Information Engineering Xi'an China Xi'An Jiaotong University School of Computer Science and Technology Xi'an China Xi'An Research Institute of Surveying and Mapping Xi'an China North China Institute of Computing Technology Beijing China

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

Kriging is a group of widely used spatial interpolation algorithms in geosciences. Ordinary Kriging is the basic of the Kriging algorithm, which is applied to a lot of research in the field of geographic information. However, with the increase in data size and its computational characteristics, the ordinary Kriging algorithm is becoming more and more computationally stressed. To solve these problems, this paper designs and implements a parallel ordinary Kriging algorithm for GPUs. The algorithm implemented in this paper considers the effect of data layouts for accelerating parallel performance on GPUs. This paper conducts a series of comparative experiments on the serial version and the parallel ordinary Kriging algorithms over different data sizes on both single and double precision. Experimental results show that the speed-up reaches up to 600 in single precision data and up to 350 in double precision data. © 2022 IEEE.

关键词： Program processors

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