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

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Optimization of Rotation-Invariant Feature Detection Method for Pedestrian Recognition

Optimization of Rotation-Invariant Feature Detection Method ...

引用

International Conference on Intelligent Informatics and Biomedical Sciences

作者： Toshiki Yahiro Kousuke Matsushima Advanced Engineering School Mechanical and Electrical Systems Engineering Advanced Course National Institute of Technology Kurume Collage Department of Control and Information Systems Engineering National Institute of Technology Kurume Collage

ISBN: (纸本)9781479985630

In the technology of advanced Safety Vehicle (ASV), it is important elements to detect pedestrians. There is a technique which used image features and a classifier for pedestrian detection. However, pedestrians have pose variations, such as a rotation. That's why we cannot always get the same features. In order to solve this problem, we can use a way which used the rotation-invariant HOG, but this method needs very high costs because it's not optimized for pedestrian detection. Therefore, we improved calculating method of the rotation-invariant HOG for pedestrian detection and compared this proposed method with the conventional method. As a result, recognition rate was turned from 53.75% to 71.7%.

关键词： Pedestrian detection ASV HOG

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Visualizations of Nonlinear Phenomena of an Inclined Cantilevers by Mathematica

Visualizations of Nonlinear Phenomena of an Inclined Cantile...

引用

International Conference on Computational Science and its Applications (ICCSA)

作者： Shuhei Miyake Ryuzaburo Sugino Tokyo University of Information Sciences Chiba Japan Department of Systems and Control Engineering Advanced Engineering Course Anan National College of Technology Tokushima Japan

In this study, numerical solution procedures by an integral equation method are presented for the large deflection problem of an inclined cantilever by the elastica theory. Inclined cantilever with a load is analyzed systematically. The problem expressed by a class of nonlinear two-point boundary value problem is transformed into an integral equation by means of integration procedure. Using our numerical scheme, torque-turning angle curves and cantilever configurations are determined for the various loading parameters. Wang's solutions are compared with our solutions obtained by integral equation method. We treat a cantilever with an end load,and various cantilever's shape showing the large deformations in which we can recognize highly nonlinear *** obtained results with various cantilever deformations are visualized by using Mathematica which is powerful computer algebra system.

关键词： Visualization Integral equations Boundary value problems Nonlinear equations Torque Mathematical model Differential equations Informatics control engineering Educational institutions

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Student modeling method integrating knowledge tracing and IRT with decay effect 2

Student modeling method integrating knowledge tracing and IR...

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2nd International Workshop on Educational Knowledge Management, EKM 2016

作者： Oeda, Shinichi Asai, Kouta Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu CityChiba Japan Advanced Control and Information Engineering Course National Institute of Technology Kisarazu College Japan NIFTY Corporation Human Resources Department Shinjuku Front Tower 21-1 Kita-shinjuku 2-chome Shinjuku-kuTokyo Japan

Educational data mining (EDM) involves the application of data mining, machine learning, and statistics to information generated from educational settings. Modeling students' knowledge is a fundamental part of intelligent tutoring systems. One of the most popular methods for estimating students' knowledge is knowledge tracing. It is the de-facto standard for inferring students' knowledge from performance data. The goal of this study is to estimate future student performance from massive amounts of examination results. We propose a novel method to improve the precision of student modeling using knowledge tracing with item response theory, including the decay theory of forgetting. © 2016, CEUR-WS. All rights reserved.

关键词： Hidden Markov models

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A design of bilateral servo system with self-tuning function

A design of bilateral servo system with self-tuning function

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International Conference on advanced Mechatronic Systems (ICAMechS)

作者： Hirofumi Ohtsuka Tomoaki Nakamura Dept. of Control and Information System Engineering Kumamoto National College of Technology Kumamoto Japan Kumamoto National College of Technology Advanced Course of Electronics and Information System Engineering Kumamoto Japan

Manual micromanipulation requires for operators to have enough experience and outstanding skill. A bilateral servo system attempts to convert difficult tasks so as to be felt easier for skilled operators. One of such a bilateral servo system, which reduces the displacement and expands the force, has been proposed by Kikuchi and Shiraishi[1]. However, concerning to the offset between a master manipulator's stiffness and a slave manipulator's one, its adverse effects of the operation stability becomes remarkable for the change of operator's dynamics. In an actual use, such offset may cause a heavy damage of the manipulated object by the operation. In this study, we propose a self-tuning mechanism which can eliminate such offset problem. And we have confirmed the effectiveness of the proposed method by numerical simulation.

关键词： Manipulators Force Numerical simulation Servomotors Steady-state

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Classification of Road Damage Images Considering the Discovery of Unknown Classes

Classification of Road Damage Images Considering the Discove...

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Automation, control and Robots (ICACR), International Conference on

作者： Hiroki Nagamatsu Kousuke Matsushima 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

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.

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Mobile Generalized Category Discovery for Road Damage Detection

Mobile Generalized Category Discovery for Road Damage Detect...

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Akashi Rintaro Kousuke Matsushima 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: (数字)9798350389210

ISBN: (纸本)9798350389227

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.

关键词： Training Deep learning Accuracy Communication systems Roads Clustering methods Supervised learning Signal processing Convolutional neural networks

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3D Map Generation for Decommissioning Work

3D Map Generation for Decommissioning Work

引用

International Conference on Intelligent Autonomous Systems (ICoIAS)

作者： Ryoma Hanabusa Yasuto Nagase Satoshi Mitsui Toshifumi Satake Naoki Igo Advanced Course of Production System Engineering National Institute of Technology Asahikawa College Asahikawa Japan Control and Information Engineering National Institute of Technology Asahikawa College Asahikawa Japan

ISBN: (数字)9781728160788

ISBN: (纸本)9781728160795

At the Fukushima Daiichi Nuclear Power Station (NPS), robots are used to propel the decommissioning work. Creating a 3D map of the internal environment of the decommissioning work is a necessary technology for improving the working efficiency of a decommissioning robot. The purpose of this research is to realize a 3D map creating a system using a camera by operating the humanoid general-purpose robot Pepper using the Robot Operating System (ROS) and implementing and executing Visual SLAM. The system aims to be applied to a decommissioning robot in the future. In this study, we implemented Visual SLAM methods R-tab Map, which is a method for constructing 3D maps in real-time, and Large-Scale Direct SLAM, which is method that generates a map using luminance with a large gradient between frames without using features for map generation. We also compared and evaluated the effectiveness of the generated maps. Besides, the robot was manually operated using nao_teleop, which can operate Pepper from the ROS library with the PS3 controller, the visualization software rviz, and the point cloud visualization library PointCloudViewer. Using two Visual SLAM's methods, we implemented the experiment. During the experiment, a chair was placed in front of Pepper as an obstacle. In this environment, Pepper was rotated 360 [deg] by manual operation. However, nao_teleop and Visual SLAM methods caused a conflict between the manual operation of Pepper and the 3D map generation process, and both processes stopped. Therefore, the 3D map wasn't generated. To resolve the conflict with operation, Pepper was moved by human and the experiment was again. As a result, the camera image obtained from Pepper was distorted, the map could not be optimized. Therefore, the part of 3D map was only generated. For this reason, to realize a 3D map generation system, it was important to properly calibrate the camera that could acquire a flat image without distortion.

关键词： Three-dimensional displays Cameras Libraries Robot vision systems

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Solving PnP Problem Using Improved Sparrow Search Algorithm

Solving PnP Problem Using Improved Sparrow Search Algorithm

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Kurihara Riku Kousuke Matsushima 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: (数字)9798350389210

ISBN: (纸本)9798350389227

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.

关键词： Point cloud compression Accuracy Three-dimensional displays Heuristic algorithms Robot vision systems Pose estimation Signal processing algorithms Search problems Cameras Optimization

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

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

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Robotics, engineering, Science, and Technology (RESTCON), International Conference on

作者： Ryoichirou Hara Kousuke Matsushima 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

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.

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

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

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Masahiko Ida Kousuke Matsushima 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: (数字)9798350389210

ISBN: (纸本)9798350389227

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.

关键词： Training Three-dimensional displays Accuracy Uncertainty Road accidents Object detection Signal processing Safety Object recognition Vehicles

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