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检索条件"机构=Department of Advanced Systems and Control Engineering"

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Three-dimensional tracking and height verification of polystyrene particles with piezo actuator positioning by means of multi-wavelength evanescent fields 20

Three-dimensional tracking and height verification of polyst...

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20th International Conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2020

作者： Blattler, Aran Khajornrungruang, Panart Suzuki, Keisuke Permpatdechakul, Thitipat Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of Intelligent and Control Systems Advanced Mechanical Division Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9780995775176

To understand the nanoparticle motion behaviours on a surface, we have studied to investigate the three-dimension tracking of an individual nanoparticle with nanometre localisation accuracy. In three-dimensional nanoparticle moving position, the X and Y coordinates are on the parallel plane to the surface, which their coordinates can be tracked in general microscopy. However, the Z coordinate is the nanoparticle height position perpendicularly to the surface plane, which could not be generally tracked without longitudinal scanning method. In this paper, the polystyrene 100 nm standard particles were used to investigate the three-dimensional tracking. The individual standard nanoparticles were fixed on the tip, which was vertically travelled with the piezo actuator for movement control on the third dimension to verify the actual height Z. Our developed optical microscopy apparatus has been applying multi-wavelength evanescent fields, and obtaining multi scattering light intensities from the observed nanoparticles near to surface. Commonly, the X and Y coordinates of nanoparticle were determined by the centre of mass of each scattering light intensity. The height Z was investigated from the integration of the detected scattering intensities of each wavelength. By using our method, the height Z uncertainty could be measured approximately less than ±20 nm, and the penetration height measurability was approximately 250 nm far from the reference surface. Consequently, we could three-dimensionally track the individual standard nanoparticle in recording speed higher than 50 frames per second. Copyright © Proceedings of the 20th International Conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2020. All rights reserved.

关键词： Nanoparticles

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Deterioration modeling strategy for pro-active services of commercial vehicles

Deterioration modeling strategy for pro-active services of c...

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作者： Prothmann, Christoph Kokes, Michael Liu, Steven Department of Advanced Engineering Daimler Trucks Daimler AG 70327 Stuttgart-Untertuerkheim Germany Institute of Control Systems University of Kaiserslautern 67663 Kaiserslautern Germany

ISBN: (纸本)9781424474264

The goal of deterioration modeling is to support the decision on pro-active replacement of parts in components that have a shorter life cycle than the vehicle (commercial vehicle) itself. A method of deterioration modeling is demonstrated and exemplified at a turbo charger that includes expertise on the components and an integrated mathematical-physical description of wear and takes into account the knowledge on endurance strength. The deterioration model input signals are the load histories of the components which have to be stored in the truck in so called load matrices. For these load matrices a storage space requirement analysis is presented that considers the operation time of the truck and the specific commercial vehicle requirements. © 2010 AACC.

关键词： Commercial vehicles

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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...

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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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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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Visual SLAM in Dynamic Environments using Multi-lens Omnidirectional Camera 4

Visual SLAM in Dynamic Environments using Multi-lens Omnidir...

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4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022

作者： Yamasaki, Shoya Matsushima, Kousuke National Insutitute of Technology Kurume College Advanced Engineering School Fukuoka Japan National Insutitute of Technology Kurume College Department of Control and Information Systems Engineering Fukuoka Japan

ISBN: (纸本)9781665419048

Many studies relate to Visual SLAM (Simultaneous Localization and Mapping) systems have been conducted for automatic driving and assisted driving systems. In this paper, we propose the Visual SLAM system combining multi-lens omnidirectional camera and object recognition. The proposed method can reduce some noises caused by moving objects, such as vehicle, pedestrian, bicycle, and so on, in the road environment. We also propose a robust SLAM system against tracking loss by improving the tracking thread. Since a multi-lens omnidirectional camera system can view not only the front but also the left and right sides of the scene, it is possible to obtain effective feature points even in scenes with little texture. Therefore, it provides better SLAM results than monocular camera systems. © 2022 IEEE.

关键词： Textures

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Image Classification for advanced Driving Assistant System in Electric Wheelchair 4

Image Classification for Advanced Driving Assistant System i...

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4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022

作者： Yamawaki, Masahiro Matsushima, Kousuke Kurume College National Institute of Technology Advanced Engineering School Fukuoka Japan Kurume College National Institute of Technology Department of Control and Information Systems Engineering Fukuoka Japan

ISBN: (纸本)9781665419048

Demands for welfare equipment such as electric wheelchairs are increasing in Japan, because of traffic accidents by the elderly. In the field of automobiles, the safety driving supports have been researched in many divergences. However, in the field of electric wheelchairs, not advanced too much. In this study, we propose a method of machine learning by Self-Supervised Learning (SSL) and a method of image classification according to the results, using actual sidewalk images as a machine learning dataset, to advanced Driving Assistant System (ADAS) of electric wheelchairs. © 2022 IEEE.

关键词： Supervised learning

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Efficient Outlier Removal Combining REPPnP and the Levenberg-Marquardt Method 6

Efficient Outlier Removal Combining REPPnP and the Levenberg...

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6th IEEE International Conference on Computer and Communications, ICCC 2020

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

ISBN: (纸本)9781728186351

The ability to remove outliers is essential for performing the Perspective-n-Point (PnP) method in practical applications. However, conventional methods have high time complexities and problems of accuracy. In this paper, we propose the method for removing outliers by combining REPPnP and the Levenberg-Marquardt(LM) method. Our method was tested based ORB-SLAM2 using the KITTI dataset. The measurements were shown to lead to an accurate rotation and transloation. Then we were able to shorten the processing time. These indicate the possibility that proposed method can be applied to practical applications has improved. © 2020 IEEE.

关键词： Statistics

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Mobile Generalized Category Discovery for Road Damage Detection

Mobile Generalized Category Discovery for Road Damage Detect...

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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...

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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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Automatic Pavement Type Recognition based on Mobile Deep Learning 4

Automatic Pavement Type Recognition based on Mobile Deep Lea...

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4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022

作者： Murasaki, Reiya Matsushima, Kousuke National Institute of Technology Kurume College Advanced Engineering School Fukuoka Japan National Institute of Technology Kurume College Department of Control and Information Systems Engineering Fukuoka Japan

ISBN: (纸本)9781665419048

Pavement deformation detection is an essential task in order to maintain driving safety. In recent years, convolutional neural networks (CNN), one of the machine learning methods, have been widely studied for pavement condition detection. In this paper, we propose a method that can achieve optimal pavement deformation detection in each region, assuming a mobile terminal. © 2022 IEEE.

关键词： Pavements

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