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检索条件"机构=Advanced Control and Information Engineering Course"

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Solving PnP Problem Using Improved Sparrow Search Algorithm

Solving PnP Problem Using Improved Sparrow Search Algorithm

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Riku, Kurihara Matsushima, Kousuke Advanced Engineering Course National Institute of Technology Kurume College Kurume Japan Department of Control and Information Systems Engineering National Institute of Technology Kurume College Kurume Japan

ISBN: (纸本)9798350389210

The Perspective-n-Point (PnP) problem is a fundamental challenge in engineering that plays a crucial role in fields such as computer vision and augmented reality. This problem aims to estimate the position and orientation (pose) of a camera from known 3D points and their corresponding 2D projections on the image plane. With recent technological advancements, application areas such as autonomous vehicles, drones, and robotics are demanding increasingly faster and more accurate camera pose estimation. To address this challenge, we have introduced an improved Sparrow Search Algorithm (SSA) with the capability to dynamically restrict the search area. While the conventional SSA demonstrated excellent optimization capabilities, it faced efficiency issues when searching in high-dimensional spaces, such as those encountered in PnP problems. Our enhancement enables the algorithm to leverage information obtained during the search process to gradually narrow down the search area, thereby more efficiently locating the optimal solution. © 2024 IEEE.

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Mobile Generalized Category Discovery for Road Damage Detection

Mobile Generalized Category Discovery for Road Damage Detect...

引用

2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Rintaro, Akashi Matsushima, Kousuke Advanced Engineering Course National Institute of Technology Kurume College Kurume Japan Department of Control and Information Systems Engineering National Institute of Technology Kurume College Kurume Japan

ISBN: (纸本)9798350389210

In recent years, there has been a considerable amount of research conducted on the topic of road damage detection using deep learning techniques, with the aim of supporting the safe driving of mobility vehicles. To date, convolutional neural networks and supervised learning with large datasets have been employed for road damage detection, demonstrating high classification accuracy. However, all images in the dataset must be manually labeled. Moreover, images cannot be classified into classes that are not included in the training dataset. The real world presents a vast array of damage types, and the unlabeled input images encompass both known and unknown classes. This renders the system unsuitable for use in providing safe driving support for mobility. Considering this background, Generalized Category Discovery (GCD) has been put forth as a potential solution, aiming to achieve accurate classification in a comprehensive setting where both known and unknown classes are present in unlabeled data. The purpose of this study is to propose a novel model for GCD with the aim of providing safe driving assistance for mobility. The model is based on the pre-trained MobileViT. To validate its performance, this paper examines its performance on the general CIFAR-10 and CIFAR-100 datasets and on a road damage-specific dataset, and discusses the challenges encountered. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification of Road Damage Images Considering the Discovery of Unknown Classes 7

Classification of Road Damage Images Considering the Discove...

引用

7th International Conference on Automation, control and Robots, ICACR 2023

作者： Nagamatsu, Hiroki Matsushima, Kousuke Kurume College National Institute of Technology Advanced Engineering Course Fukuoka Japan Kurume College National Institute of Technology Department of Control and Information Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350302882

In this paper, we propose a classification method for road damage images considering mobile devices. In the general deep learning approach, it is difficult to find unknown classes. Therefore, we address the problem of Novel Class Discovery (NCD). In addition, mobile devices have limited resources. Thus, we need to reduce the number of parameters in the neural network. To solve these problems, We propose to use lightweight vision transformers (ViTs) for the encoder part of UNified Objective Function (UNO), an unsupervised learning method. Furthermore, we show the effectiveness of ViT by comparing our method to convolutional neural networks (CNNs) with a similar number of parameters. © 2023 IEEE.

关键词： Damage detection

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Multi-Lens Visual SLAM with Integrated Moving Object Removal using Unsupervised Models 1

Multi-Lens Visual SLAM with Integrated Moving Object Removal...

引用

1st International Conference on Robotics, engineering, Science, and Technology, RESTCON 2024

作者： Hara, Ryoichirou Matsushima, Kousuke Kurume College Advanced Engineering Course National Institute of Technology Fukuoka Japan Kurume College National Institute of Technology Department of Control and Information Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350348842

In advanced Driver-Assistance Systems (ADAS), SLAM (Simultaneous Localization and Mapping) technology is required to accurately estimate the position and orientation of onboard cameras. Compared to LiDAR SLAM, Visual SLAM has the advantage of collecting rich environmental information at a lower cost. However, the accuracy of the estimated pose and map construction is greatly reduced by moving objects in the camera scene. To solve this problem, a multi-camera SLAM has been proposed, which works robustly in dynamic environments by using multiple cameras to acquire more feature points from omnidirectional environmental information. In this paper, we propose a robust SLAM system based on a multi-camera SLAM system combined with object detection and semantic segmentation networks. We then evaluate the robustness of the proposed method by comparing it to SLAM systems that do not remove moving objects. Experimental results showed that the Absolute Trajectory Error and the Relative Position Error performed better than conventional SLAM by masking moving objects. It also showed that more static objects were detected and constructed in the map. © 2024 IEEE.

关键词： Object detection

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Uncertainty-aware Monocular 3D Object Detection for Safe Driving Assistance

Uncertainty-aware Monocular 3D Object Detection for Safe Dri...

引用

2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Ida, Masahiko Matsushima, Kousuke Advanced Engineering Course National Institute of Technology Kurume College Fukuoka Japan Department of Control and Information Systems Engineering National Institute of Technology Kurume College Fukuoka Japan

ISBN: (纸本)9798350389210

Safety is of utmost importance when utilizing mobile vehicles. The introduction of safe driver assistance and automated driving systems will not only solve this problem, but also reduce road accidents and make driving smoother. To achieve these goals, 3D object detection is an essential technology and various methods have been proposed. Among these, monocular 3D object detection is an important technique and it is very difficult to identify the dimensions, direction and position of an object from a monocular image. Previous methods have predicted the dimensions directly from monocular images, resulting in poor accuracy of the dimensions in the depth direction. However, this paper proposes an indirect method for estimating the dimensions in the depth direction by predicting the volume of 3D bounding box. Detection accuracy for distant and partially visible objects is improved by using a loss function that uses uncertainty to account for the variability in predictions that occurs during learning. The proposed method has received an excellent evaluation on KITTI dataset. © 2024 IEEE.

关键词： 3D modeling

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Automatic Pavement Crack Detection using Multi-Scale Image Neighborhoods information

Automatic Pavement Crack Detection using Multi-Scale Image N...

引用

2019 International Conference on Mechatronics, Robotics and Systems engineering, MoRSE 2019

作者： Komori, Taiki Matsushima, Kousuke Takahashi, Osamu Advanced Engineering Course National Institute of Technology Kurume College Fukuoka Japan National Institute of Technology Kurume College Department of Control and Information Systems Engineering Fukuoka Japan Nagaoka University of Technology Department of Civil and Environmental Engineering Niigata Japan

ISBN: (纸本)9781728139845

Pavement cracks are dangerous because they can cause accidents such as tire punctures, slips, and collapses. Therefore, it is necessary to repair them properly. In recent years, various crack detection methods using pavement images have been proposed. However, in many cases, there are problems with accuracy and processing time. In this paper, we propose a new crack detection method using multi-scale image and neighborhood information. Experimental results show that the proposed method is superior to the most advanced crack detection methods in both accuracy and processing time. © 2019 IEEE.

关键词： Crack detection

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Crack Detection using Spectral Clustering: Self-Tuning Considering Crack Feature and Connections

Crack Detection using Spectral Clustering: Self-Tuning Consi...

引用

2019 International Conference on Mechatronics, Robotics and Systems engineering, MoRSE 2019

作者： Shiotsuka, Daiki Matsushima, Kousuke Takahashi, Osamu Advanced Engineering Course National Institute of Technology Kurume College Kurume Japan National Institute of Technology Kurume College Department of Control and Information Systems Engineering Kurume Japan Nagaoka University of Technology Department of Civil and Environmental Engineering Nagaoka Japan

ISBN: (纸本)9781728139845

Cracks on the pavement road cause various traffic problems. Hence, we should repair them properly. Nowadays, there are a variety of crack detection method based on computer vision for operation efficiency. Spectral Clustering is one of them and effective. However, detection accuracy may decrease depending on the image because roads often have many bumps. In this paper, we cluster images having less noise into two clusters, while noisy images into three clusters. The number of clusters is determined automatically by considering the relationship between crack feature and the number of connections. © 2019 IEEE.

关键词： Pavements

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Data Preprocessing with IRM for skill extraction using NMF 26

Data Preprocessing with IRM for skill extraction using NMF

引用

26th International Conference on Knowledge-Based and Intelligent information and engineering Systems, KES 2022

作者： Oeda, Shinichi Iizumi, Shunsuke Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan

To provide examinees with appropriate question items, identifying and evaluating users' latent and acquired skills is important in e-learning systems. These latent skills are defined in terms of a Q-matrix. Constructing Q-matrices manually is relatively labor-intensive, because doing so requires expert insight as well as verbal examination to identify which skills students have mastered. Methods to extract a Q-matrix from examination results automatically using nonnegative matrix factorization (NMF) have been explored, and improved methods such as online NMF have been proposed, which extracts an immutable Q-matrix from time-series data. In this study, we propose a data preprocessing method to improve the accuracy of NMF using a co-clustering method called an infinite relational model (IRM). We also describe experiments conducted using synthetic data, in which learners'skills and the bias in the number of their responses were evaluated, and show that the results demonstrate the efficacy of the proposed approach. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent information & engineering Systems (KES 2022)

关键词： Non-negative matrix factorization

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Latent State Estimation Method for Students Using Knowledge Tracing Combining with IRT 26

Latent State Estimation Method for Students Using Knowledge ...

引用

26th International Conference on Knowledge-Based and Intelligent information and engineering Systems, KES 2022

作者： Oeda, Shinichi Kakizaki, Toma Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan

The Intelligent Tutoring Systems (ITSs) are a system that provides an efficient learning environment by assigning questions that are suitable to the learner's skill state. To improve the performance of ITSs, conventional studies have proposed student models that can estimate skill states with high accuracy. However, these models were based on the assumption that the log data was correct. In other words, they do not take into account the possibility of students cheating or memorizing answers, assuming that the test was conducted fairly and impartially. In recent years, many examinations have been conducted online as a countermeasure against COVID-19 (coronavirus) infections, and students may be able to obtain hints for the examinations using the internet or books. In this way, it is important to know how students approach learning in order to estimate their skill state. In this study, we propose a method for estimating the latent state of learners using a model that combines Knowledge Tracing, the de-facto standard for student modeling method, and Item Response Theory. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent information & engineering Systems (KES 2022)

关键词： Students

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Proposal of a novel peeling of nano-particle (PNP) process for localized material removal on a hard material surface by controllable magnetic fields 22

Proposal of a novel peeling of nano-particle (PNP) process f...

引用

22nd International Conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2022

作者： Permpatdechakul, Thitipat Khajornrungruang, Panart Suzuki, Keisuke Kutomi, Shotaro Graduate School of Computer Science and System Engineering Mechanical Information Science and Technology Course Kyushu Institute of Technology Fukuoka Japan Department of Intelligent and Control Systems Avanced Mechanical Division Kyushu Institute of Technology Fukuoka Japan School of Computer Science and System Engineering Advanced Mechanical Course Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9781998999118

This article proposes a novel Peeling of Nano-Particle (PNP) process to locally remove material on a hard material surface using controllable magnetic fields. Fe3O4 particles in the size range of 50-100 nm in aqueous solution (pH value 11 adjusted by KOH) were magnetically controlled to adhere and remove from a 4H-SiC surface by two electromagnetic coils, which were sandwiched between the substrate. The particle controlling phenomenon on the surface was experimentally observed and verified by our developed apparatus applying an evanescent field. According to the results, particles could be controlled to approach and remove from the substrate surface with 12mT of magnetic field at the selective area on the substrate surface to realise the proposed PNP process. © European Society for Precision engineering and Nanotechnology, Conference Proceedings - 22nd International Conference and Exhibition, EUSPEN 2022. All rights reserved.

关键词： Magnetic fields

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