研究等業績 - その他 - 鳥屋　剛毅

Deep learning-based rock type identification using drill vibration frequency spectrum images

Senjoba L.

International Journal of Mining, Reclamation and Environment ( International Journal of Mining, Reclamation and Environment )  39 ( 1 ) 40 - 55   2025年

DOI

Utilizing convolutional autoencoders for anomaly detection and drill bit damage assessment in rotary percussion drilling

Senjoba L.

Journal of Sustainable Mining ( Journal of Sustainable Mining )  24 ( 2 ) 282 - 298   2025年

DOI

Advanced UAV photogrammetry for precision 3D modeling in GPS denied inaccessible tunnels

Ikeda H.

Safety in Extreme Environments ( Safety in Extreme Environments )  6 ( 4 ) 269 - 287   2024年12月

DOI

Predicting ground vibration during rock blasting using relevance vector machine improved with dual kernels and metaheuristic algorithms

Fissha Y.

Scientific Reports ( Scientific Reports )  14 ( 1 )   2024年12月

DOI

Cone penetration test-based assessment of liquefaction potential using machine and hybrid learning approaches

Khatti J.

Multiscale and Multidisciplinary Modeling, Experiments and Design ( Multiscale and Multidisciplinary Modeling, Experiments and Design )  7 ( 4 ) 3841 - 3864   2024年09月

DOI

Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach

Senjoba L.

Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) )  14 ( 9 )   2024年05月

DOI

Integrated spectral image registration protocol for enhanced land pollution assessment and management: bridging satellite and drone data for high-resolution mapping

Okada N.

Journal of Material Cycles and Waste Management ( Journal of Material Cycles and Waste Management )  27 ( 2 ) 685 - 698   2024年

DOI

A Comprehensive Numerical Modeling Study for Parameter Optimization and Slope Stability Analysis in the Baganuur Lignite Coal Mine

Enkhbold B.

Mining ( Mining )  3 ( 4 ) 755 - 772   2023年12月

DOI

Evaluation and Prediction of Blast-Induced Ground Vibrations: A Gaussian Process Regression (GPR) Approach

Fissha Y.

Mining ( Mining )  3 ( 4 ) 659 - 682   2023年12月

DOI

Digital Twin Technology in Data Center Simulations: Evaluating the Feasibility of a Former Mine Site

Ikeda H.

Sustainability (Switzerland) ( Sustainability (Switzerland) )  15 ( 23 )   2023年12月

DOI

Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach

Ikeda H.

Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) )  13 ( 23 ) 12674   2023年12月

<jats:p>Blasting is routinely carried out in urban quarry sites. Residents or houses around quarry sites are affected by the ground vibrations induced by blasting. Peak Particle Velocity (PPV) is used as a metric to measure ground vibration intensity. Therefore, many prediction models of PPV using experimental methods, statistical methods, and Artificial Neural Networks (ANNs) have been proposed to mitigate this effect. However, prediction models using experimental and statistical methods have a tendency of poor prediction accuracy. In addition, while prediction models using ANNs can produce a highly accurate prediction results, a large amount of measured data is necessarily collected. In an urban quarry site where the number of blastings is limited, it is difficult to collect a lot of measured data. In this study, a new PPV prediction method using Weighted Non-negative Matrix Factorization (WNMF) is proposed. WNMF is a method that approximates a non-negative matrix (including missing data) to the product of two low-dimensional matrices and predicts the missing data. In addition, WNMF is one of the unsupervised learning methods, so it can predict PPV regardless of the amount of data. In this study, PPV was predicted using measured data from 100 sites at the Mikurahana quarry site in Japan. As a result, the proposed method showed higher accuracy when using measured data at 60 sites rather than 100 sites, and the root mean square error for PPV prediction decreased from 0.1759 (100 points) to 0.1378 (60 points).</jats:p>

DOI CiNii Research

Deep Learning-Based Estimation of Muckpile Fragmentation Using Simulated 3D Point Cloud Data

Ikeda H.

Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) )  13 ( 19 ) 10985   2023年10月

<jats:p>This research introduces an innovative technique for estimating the particle size distribution of muckpiles, a determinant significantly affecting the efficiency of mining operations. By employing deep learning and simulation methodologies, this study enhances the precision and efficiency of these vital estimations. Utilizing photogrammetry from multi-view images, the 3D point cloud of a muckpile is meticulously reconstructed. Following this, the particle size distribution is estimated through deep learning methods. The point cloud is partitioned into various segments, and each segment’s distinguishing features are carefully extracted. A shared multilayer perceptron processes these features, outputting scores that, when consolidated, provide a comprehensive estimation of the particle size distribution. Addressing the prevalent issue of limited training data, this study utilizes simulation to generate muckpiles and consequently fabricates an expansive dataset. This dataset comprises 3D point clouds and corresponding particle size distributions. The combination of simulation and deep learning not only improves the accuracy of particle size distribution estimation but also significantly enhances the efficiency, thereby contributing substantially to mining operations.</jats:p>

DOI CiNii Research

Vehicle Localization in a Completed City-Scale 3D Scene Using Aerial Images and an On-Board Stereo Camera

Zhang H.

Remote Sensing ( Remote Sensing )  15 ( 15 ) 3871   2023年08月

<jats:p>Simultaneous Localization and Mapping (SLAM) forms the foundation of vehicle localization in autonomous driving. Utilizing high-precision 3D scene maps as prior information in vehicle localization greatly assists in the navigation of autonomous vehicles within large-scale 3D scene models. However, generating high-precision maps is complex and costly, posing challenges to commercialization. As a result, a global localization system that employs low-precision, city-scale 3D scene maps reconstructed by unmanned aerial vehicles (UAVs) is proposed to optimize visual positioning for vehicles. To address the discrepancies in image information caused by differing aerial and ground perspectives, this paper introduces a wall complementarity algorithm based on the geometric structure of buildings to refine the city-scale 3D scene. A 3D-to-3D feature registration algorithm is developed to determine vehicle location by integrating the optimized city-scale 3D scene with the local scene generated by an onboard stereo camera. Through simulation experiments conducted in a computer graphics (CG) simulator, the results indicate that utilizing a completed low-precision scene model enables achieving a vehicle localization accuracy with an average error of 3.91 m, which is close to the 3.27 m error obtained using the high-precision map. This validates the effectiveness of the proposed algorithm. The system demonstrates the feasibility of utilizing low-precision city-scale 3D scene maps generated by unmanned aerial vehicles (UAVs) for vehicle localization in large-scale scenes.</jats:p>

DOI CiNii Research

Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration

Fissha Y.

Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) )  13 ( 5 ) 3128   2023年03月

<jats:p>Rock blasting is one of the most common and cost-effective excavation techniques. However, rock blasting has various negative environmental effects, such as air overpressure, fly rock, and ground vibration. Ground vibration is the most hazardous of these inevitable impacts since it has a negative impact not only on the environment of the surrounding area but also on the human population and the rock itself. The PPV is the most critical base parameter practice for understanding, evaluating, and predicting ground vibration in terms of vibration velocity. This study aims to predict the blast-induced ground vibration of the Mikurahana quarry, using Bayesian neural network (BNN) and four machine learning techniques, namely, gradient boosting, k-neighbors, decision tree, and random forest. The proposed models were developed using eight input parameters, one output, and one hundred blasting datasets. The assessment of the suitability of one model in comparison to the others was conducted by using different performance evaluation metrics, such as R, RMSE, and MSE. Hence, this study compared the performances of the BNN model with four machine learning regression analyses, and found that the result from the BNN was superior, with a lower error: R = 0.94, RMSE = 0.17, and MSE = 0.03. Finally, after the evaluation of the models, SHAP was performed to describe the importance of the models’ features and to avoid the black box issue.</jats:p>

DOI CiNii Research

Research on vibration-based early diagnostic system for excavator motor bearing using 1-D CNN

Yandagsuren D.

Journal of Sustainable Mining ( Journal of Sustainable Mining )  22 ( 1 ) 65 - 80   2023年

DOI リポジトリ

A Method for Completing Missing 3D Point Cloud Reconstructed from Aerial Multi-View Images Using Self-Attention Mechanism

Kiyama T.

International Geoscience and Remote Sensing Symposium (IGARSS) ( International Geoscience and Remote Sensing Symposium (IGARSS) )  2023-July   927 - 930   2023年

DOI

An image-capturing system to generate 3D borehole models using multiple fiberscope cameras

Qin J.

Proceedings of SPIE - The International Society for Optical Engineering ( Proceedings of SPIE - The International Society for Optical Engineering )  12592   2023年

DOI

A Comprehensive Numerical Modeling Study for Parameter Optimization and Slope Stability Analysis in the Baganuur Lignite Coal Mine

鳥屋 剛毅

Mining   3   755 - 772   2023年  [査読有り]

CiNii Research

A Method for Completing Missing 3D Point Cloud Reconstructed from Aerial Multi-View Images Using Self-Attention Mechanism

宍戸 英彦, 鳥屋 剛毅, 北原 格

Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS2023)   1   927 - 930   2023年  [査読有り]

CiNii Research

Development of hyperspectral database and web based classifying system for rock type identification

鳥屋 剛毅, 川村 洋平

Rock Mechanics and Engineering Geology in Volcanic Fields   1   442 - 446   2023年  [査読有り]

CiNii Research