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检索条件"机构=Advanced Course in Industrial and Systems Engineering National Institute of Technology"

共 764 条记录，以下是1-10 订阅

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Current Vector Control of AC Servo Motor Considering Output Delay of PWM Inverter

Current Vector Control of AC Servo Motor Considering Output ...

引用

18th IEEE International Conference on advanced Motion Control, AMC 2024

作者： Shimamoto, Haruta Yoshioka, Takashi Urushihara, Shiro National Institute of Technology Kagawa College Advanced Course in Industrial and Systems Engineering Takamatsu Japan National Institute of Technology Kagawa College Dept. of Electrical and Computer Engineering Takamatsu Japan

ISBN: (纸本)9798350342819

In this paper, to expand a control bandwidth of AC servo motor, a new current vector control considering an output delay of PWM Inverter is proposed. The proposed current control is a I-P control with an additional feedback gain, which is based on an extended state feedback consisted from a d-q motor model, the output delay of PWM inverter and an integrator of ACR. In addition, the proposed method is easy to implement to the existing AC servo system. The effectiveness of proposed method is validated in a computer simulation and an experiments using a 3.7 kW class motor test bench. In the condition that a cut-off frequency of ACR is close to a carrier frequency, the simulation results show that the proposed method dramatically improves the current response at the higher cut-off frequency. Moreover, the experimental results show that the proposed method dramatically improves the current response at each operating point. From the results, the proposed method is effective to the expand the bandwidth of AC servo motor. © 2024 IEEE.

关键词： Electric current control

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Solving PnP Problem Using Improved Sparrow Search Algorithm

Solving PnP Problem Using Improved Sparrow Search Algorithm

引用

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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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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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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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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Feature Extraction of Gait Data via PCA and Personal Identification Using Neural Networks 9

Feature Extraction of Gait Data via PCA and Personal Identif...

引用

9th International Conference on Big Data Analytics, ICBDA 2024

作者： Nishimura, Kazuki Ishida, Akio Murakami, Jun Yamamoto, Naoki National Institute of Technology Kumamoto College Advanced Course of Electronics and Information Systems Engineering Kumamoto Japan National Institute of Technology Kumamoto College Faculty of Liberal Arts Kumamoto Japan National Institute of Technology Kumamoto College Faculty of Electronics and Information Systems Engineering Kumamoto Japan

ISBN: (纸本)9798350352962

Gait analysis is used in a wide range of fields, including clinical applications such as diagnosis and rehabilitation, investigation of gender and racial differences, and personal identification, which is important in forensic investigations and security. In recent years, gait data can be obtained from force plates, 3D motion capture, RGB cameras, and so on. In this study, we applied principal component analysis (PCA) to gait data captured with an RGB camera and analyzed the main data characteristics. However, the data used here is joint information whose posture is estimated using neural networks (NN) from gait videos taken with the camera. Furthermore, we created a NN model for personal identification using data whose dimensionality was reduced using PCA and attempted the identification. © 2024 IEEE.

关键词： Phishing

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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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Fabric Defect Detection System using YOLO 14

Fabric Defect Detection System using YOLO

引用

14th IIAI International Congress on advanced Applied Informatics, IIAI-AAI 2023

作者： Kawaguchi, Masashi Morimitsu, Yuta Watanabe, Ryusei Ikuta, Chihiro Ishii, Naohiro Umeno, Masayoshi National Institute of Technology Suzuka College Department of Electrical and Electronic Engineering Suzuka Japan Advanced Institute of Industrial Technology Information Systems Architecture Course Tokyo Japan Chubu University Department of Electronic Engineering Kasugai Japan

ISBN: (纸本)9798350324228

In textile processing companies, after purchasing fabric, dyeing, and other treatments, such as surface finishing are carried out before shipping. Defect detection technology is one of the most important issues in this field. Because if we ship a defective fabric without noticing the defect, it will cause damage to the delivery destination. Previously, defect detection of fabric has been performed using methods such as CNN. However, conventional methods such as general multilayer neural networks require two separate processing stages: object detection and identification. Therefore, there is a problem that sufficient real-time performance cannot be guaranteed in the inspection process on an actual manufacturing line. Therefore, in this study, we construct a fabric defect detection system using YOLO which is capable of real-time object detection. In the future, we aim to realize the automation of textile defect detection systems by the construction of an image recognition system using machine learning. It is expected that this will result from reduced detection errors by the human eye, improve detection accuracy, and reduce labor costs. © 2023 IEEE.

关键词： Machine learning

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Research on Metamaterial technology for Microwave Wireless Power Transfer

Research on Metamaterial Technology for Microwave Wireless P...

引用

2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Yamamoto, Tsunayuki Kobashi, Takeo Maruyama, Tamami Department of Integrated Science and Technology National Institute of Technology Tsuyama College Tsuyama-shi Japan Advanced Electronics and Information System Engineering Course National Institute of Technology Tsuyama College Tsuyama-shi Japan Department of Production Systems Engineering Institute of Technology Hakodate College Hakodate-shi Japan

ISBN: (纸本)9798350364774

In this paper, two technologies of metamaterial for microwave wireless power transfer are shown. One is a zeroth-order resonance for a microwave snow melting system, and another one is a focusing reflector for realizing of a highly efficient wireless power transfer space. A zeroth-order resonance leads a uniformed microwave heating and a flexibility of feeding in a snow melting system. Focusing reflector can make some electromagnetic hot and null points in a space. These are expected to be required technologies for highly efficient wireless power transfer. © 2024 IEEE.

关键词： Reflection

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One Solution to the Cost-Sharing Issue by the Sweden Game Experiment and Educational Effects 15

One Solution to the Cost-Sharing Issue by the Sweden Game Ex...

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15th International Congress on advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Kawaguchi, Masashi Kusaka, Takashi Ishii, Naohiro Umeno, Masayoshi National Institute of Technology Suzuka College Department of Electrical & Electronic Engineering Suzuka Japan National Institute of Technology Suzuka College Department of General Education Suzuka Japan Information Systems Architecture Course Advanced Institute of Industrial Technology Tokyo Japan Chubu University Department of Electronic Engineering Kasugai Japan

ISBN: (纸本)9798350383829

The issue of cost-sharing has been familiar since ancient times. Even today, it is used in various situations, such as cost-sharing for infrastructure development and contributions to neighborhood associations. Many methods have been proposed for the cost-sharing problem. One typical solution is the Shapley value. However, there are cases where the best solution cannot be obtained. In addition, if the solution is disadvantageous to the parties, there is a risk of dissatisfaction and failure to reach an agreement. On the other hand, gaming solutions are attracting attention. A mutually satisfactory solution can be obtained by gaming by solving the cost-sharing problem between the parties. Furthermore, better solutions than other methods have been derived. In this paper, we report on the optimal solution derived from the gaming results, its educational effects, and the results of a webinar on gaming in Finland and Japan. © 2023 IEEE.

关键词： Cost effectiveness

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