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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Setiawan, Yosua Puji, Muhammad Nurul Astuti, Winda Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368918

Road accidents have become a problem for many road users in many countries. It may affect human daily activity and tasks, potentially influencing productivity and safety. Therefore, reducing road accidents as swiftly and effectively as possible is critical. For this reason, a Traffic signs detection system has been developed in this work to improve road safety. The detection system can detect traffic signs on the road such as traffic lights, speed limits, and no U-turn signs, and then it will provide users with the corresponding text on an LCD and vibration feedback using a motor. Throughout this work, the You Only Look Once (YOLO) algorithm is proposed for detecting traffic signs on the road. Moreover, a camera is used to extract live videos which serve as an input for the Raspberry Pi 4 model B containing custom YOLOv5 and YOLOv8 models. The system then sends the output to an LCD, a vibration motor, and a radio frequency (RF) module as notifications for users. In the end, the performance of the proposed method has been collected and it is found to produce good accuracy within an inconsiderable amount of 1.677 images and 2.263 annotations. Therefore, the accuracy of 0.69 for YOLOv5 and 0.7 for YOLOv8 were obtained from the training and validation process. Furthermore, the system accurately detects the traffic signs on the road. © 2024 IEEE.

关键词： Traffic signs

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Merge LiDAR and High-precision Digital Map for Real-time Object Detection and GNSS Heading Calibration

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Journal of Institute of Control, robotics and Systems 2023年第4期29卷 301-307页

作者： Sim, Hongseok Baek, Seungbeom Lee, Jaewook Moon, Heechang Industrial Convergence Interdepartmental Program Autonomous Vehicle·Intelligent Robotics Hongik University Korea Republic of Mechanical and System Design Engineering Hongik University Korea Republic of

In this study, a method was proposed for correcting the object positioning error of light detection and ranging (LiDAR) due to the roll, pitch, and yaw data error of the inertial measurement unit sensor in the global navigation satellite system (GNSS) sensor by using a high-precision digital map. A real-time obstacle map was formed using Velodyne’s three-dimensional lines, VLP-32 and VLP-16. The high-precision digital map was then merged into the LiDAR map. The map obtained by combining LiDAR and high-precision digital maps did not match because of certain errors. To eliminate these errors, a method of fitting a map with high precision on the XY plane was established, and fittable criteria were determined. Algorithm verification was performed during actual driving, and errors were removed in real time. Various autonomous driving technologies can be developed using the sensor error data preprocessing process. Copyright© ICROS 2023.

关键词： Errors

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Design and Development of an Internet of Things (IoT)-Based Application and System to Detect Stunting in Infants and Toddlers 10

Design and Development of an Internet of Things (IoT)-Based ...

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10th International HCI and UX Conference in Indonesia, CHIuXiD 2024

作者： Tiffany, Theresa Jeremia, James Lie, Zener Sukra Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Engineering Program Computer Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350380439

Currently, the process of monitoring children's growth in Indonesia relies on manual methods for collecting anthropometric data. These methods pose a risk of data recording errors. Additionally, the ratio of healthcare workers to children in Indonesia is 1:90, a significant disparity that hinders efforts to reduce the prevalence of stunting in the country. To support the third Sustainable Development Goal (SDG), which emphasizes 'Good Health and Well-being,' it is crucial to address issues in the stunting management process, from data collection to monitoring. The design and development of this iOS-based mobile application aim to assist in monitoring child growth and provide guidance and steps for prevention and treatment. Besides application development, it also involves the design and development of an Internet of Things (IoT) system to aid in the collection of children's anthropometric data. The research methodology includes studying stunting cases in Indonesia, anthropometric data calculation methods, stunting prevention and management steps, application design and development, automatic anthropometric data collection, and online data storage and transmission using IoT technology. The developed application and system have been tested and successfully integrated. The IoT device has an accuracy rate of 99.83% for weight measurement calibration and 98.68% for height or length measurement calibration. From the testing results of the child condition calculation method in the application, an accuracy rate of 100% was achieved. © 2024 IEEE.

关键词： Anthropometry

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Reachability-Based Delayed Decision Guidance for Conventional Interceptors 63

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63rd Israel Annual Conference on Aerospace Sciences, IACAS 2024

作者： Shalumov, Vitaly Merkulov, Gleb Shima, Tal Technion – Israel Institute of Technology Haifa3200003 Israel The Technion Program for Autonomous Systems and Robotics Israel Faculty of Aerospace Engineering Israel

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Multi-Robot Planning for Filming Groups of Moving Actors Leveraging Submodularity and Pixel Density 63

Multi-Robot Planning for Filming Groups of Moving Actors Lev...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Hughes, Skyler Martin, Rebecca Corah, Micah Scherer, Sebastian Program Carnegie Mellon University Nsf Reu Program United States Robotics Institute Carnegie Mellon University Airlab United States Colorado School of Mines Department of Computer Science United States

ISBN: (纸本)9798350316339

Observing and filming a group of moving actors with a team of aerial robots is a challenging problem that combines elements of multi-robot coordination, coverage, and view planning. A single camera may observe multiple actors at once, and a robot team may observe individual actors from multiple views. As actors move about, groups may split, merge, and reform, and robots filming these actors should be able to adapt smoothly to such changes in actor formations. Rather than adopt an approach based on explicit formations or assignments, we propose an approach based on optimizing views directly. We model actors as moving polyhedra and compute approximate pixel densities for each face and camera view. Then, we propose an objective that exhibits diminishing returns as pixel densities increase from repeated observation. This gives rise to a multi-robot perception planning problem that we solve via a combination of value iteration and greedy submodular maximization. We evaluate our approach on challenging scenarios modeled after various social behaviors and featuring different numbers of robots and actors and observe that robot assignments and formations arise implicitly given the movements of groups of actors. Simulation results demonstrate that our approach consistently outperforms baselines, and in addition to performing well with the planner's approximation of pixel densities our approach also performs comparably for evaluation based on rendered views. Overall, the multi-round variant of the sequential planner we propose meets (within 1%) or exceeds formation and assignment baselines in all scenarios. © 2024 IEEE.

关键词： Multipurpose robots

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Histogram of Oriented Gradients (HOG) and Haar Cascade with Convolutional Neural Network (CNN) Performance Comparison in the Application of Edge Home Security System 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Asy’ari, Muhammad Zacky Filbert, Sebastian Sukra, Zener Lie Automotive and Robotics Program Computer Engineering Department BINUS ASO of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

In recent years, security has played a significant role in our daily lives. House security has become more popular with the enhancement of the Internet of Things (IoT) in intelligent home automation. This paper aims to build a system that can detect faces and the presence of unknown people using computer vision methods combined with the Internet of things. Two algorithms, Histogram of Oriented Gradients (HOG) and Haar Cascade classifiers with the enhancement of Convolutional Neural Network (CNN), were used to compare their performance. The system utilizes Nvidia Jetson Nano as an edge device, HD camera, Light Emitting Diode (LED), and sound buzzer as peripheral hardware. Telegram messenger application was used to notify the homeowner when uninvited guests passed through the pre-defined security area. In the result, the HOG method has 100% accuracy compared to 75% Haar Cascade with 720p video quality in the proposed developed system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Internet of things

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Multitask Lower Limb Classification System for Dorsiflexion Rehabilitation by Using Electroencephalography and Functional Electrical Stimulation

Multitask Lower Limb Classification System for Dorsiflexion ...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Gonzalez-Cely, A.X. Dantas, André Felipe Oliveira De Azevedo Santo, Caroline Cunha Do Espirito Bastos-Filho, Teodiano F. Delisle-Rodriguez, Denis Federal University of Espirito Santo Robotics and Assistive Technology Laboratory Vitoria Brazil Santos Dumont Institute Postgraduate Program In Neuroengineering Macaiba Brazil

ISBN: (纸本)9798350361025

Providing alternatives for lower limb moment control in post-stroke or post-Spinal Cord Injury (SCI) patients who have suffered a severe neural impairment many years ago can further improve the effect of rehabilitation. This work proposes a multitask system for dorsiflexion of the right, left, and both feet through Electroencephalography and Functional Electrical Stimulation (FES). A Butterworth bandpass filter between 8 and 30 Hz is implemented, and subsequently, three strategies are developed by first considering the power spectral density features in mu, low-beta, and high-beta bands responsible for motor task execution. Moreover, the second strategy uses Common Spatial Patterns filters, and the third strategy uses Riemannian Geometry (RG) to extract features. Four distinct classifiers were explored: k-nearest neighbors, support vector machine, Linear Discriminant Analysis (LDA), and decision tree. Based on Accuracy (ACC), True Positive Rate (TPR), and False Positive Rate (FPR) were evaluated the classifiers and the strategies implemented by achieving an ACC of 0.79, a TPR of 0.89 and a FPR of 0.05 employing RG + LDA. The implemented methodology allows the feasibility of a Human-Machine Interface (HMI) which implements a lower-limb MI task recognition system to be used in rehabilitation. Future work will implement the recognition system in real-time to users move the desired limb by activating a FES that generates contraction. © 2024 IEEE.

关键词： Electroencephalography

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Design and Implementation of Dynamic Motion Policy for Multi-Sensor Omnidirectional Mobile Arm Robot System in Human-Robot Coexisted Healthcare Workplace

Design and Implementation of Dynamic Motion Policy for Multi...

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2023 International Conference on Advanced robotics and Intelligent Systems, ARIS 2023

作者： Wu, Yu-Hsiung Li, Tzuu-Hseng S. National Cheng Kung University Ms Degree Program on Ai Robotics AiRobots Lab. Tainan Taiwan

ISBN: (纸本)9798350302714

This paper presents the design and implementation of an Omnidirectional Mobile Arm Robot System (O-MARS) equipped with multiple sensors. The system integrates sensor information using Riemannian Motion Policies (RMP) to generate dynamic motion policies, which are continuously transmitted to the corresponding actuators of the robot. To facilitate dynamic motion policies for the robot arm and mobile robot, this paper incorporates Eye-in-Hand and Eye-to-Hand vision applications as well as self-collision avoidance. The integration of vision and LIDAR localization guides the mobile robot towards its goal position. Additionally, a virtual model of the robot's environment is constructed, and a virtual-physical integration system is devised. Within this system, the decision-making process of the RMP involves establishing relevant connections between recognized modeled environmental obstacles and the robot's digital twins. This capability enables the mobile robot to navigate around obstacles in a familiar environment. Furthermore, the inclusion of digital twins allows for the integration of LiDAR obstacle avoidance, empowering the robot to exhibit avoidance behavior when encountering unknown obstacles. The system also incorporates human skeletons and whole-body position information to autonomously analyze collision dynamics between humans and robots, ensuring their safe coexistence in shared environments. Lastly, a physical experiment is conducted in a healthcare workplace where humans and robots coexist, with a specific focus on the transportation of medical supplies. This experiment aims to demonstrate the system's effectiveness in terms of accuracy, efficiency, and safety. This paper applies the RMP approach to various domains, including sensor integration, virtual-physical integration, and the heterogeneous application of manipulators and mobile robots. The experimental results of this paper can be found at https://***/watch?v=pRyd0-gvutM. © 2023 IEEE.

关键词： Mobile robots

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ED-NERF: EFFICIENT TEXT-GUIDED EDITING OF 3D SCENE WITH LATENT SPACE NERF 12

ED-NERF: EFFICIENT TEXT-GUIDED EDITING OF 3D SCENE WITH LATE...

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12th International Conference on Learning Representations, ICLR 2024

作者： Park, Jangho Kwon, Gihyun Ye, Jong Chul Kim Jaechul Graduate School of AI Korea Republic of Robotics Program Korea Republic of Department of Bio and Brain Engineering KAIST Korea Republic of

Recently, there has been a significant advancement in text-to-image diffusion models, leading to groundbreaking performance in 2D image generation. These advancements have been extended to 3D models, enabling the generation of novel 3D objects from textual descriptions. This has evolved into NeRF editing methods, which allow the manipulation of existing 3D objects through textual conditioning. However, existing NeRF editing techniques have faced limitations in their performance due to slow training speeds and the use of loss functions that do not adequately consider editing. To address this, here we present a novel 3D NeRF editing approach dubbed ED-NeRF by successfully embedding real-world scenes into the latent space of the latent diffusion model (LDM) through a unique refinement layer. This approach enables us to obtain a NeRF backbone that is not only faster but also more amenable to editing compared to traditional image space NeRF editing. Furthermore, we propose an improved loss function tailored for editing by migrating the delta denoising score (DDS) distillation loss, originally used in 2D image editing to the three-dimensional domain. This novel loss function surpasses the well-known score distillation sampling (SDS) loss in terms of suitability for editing purposes. Our experimental results demonstrate that ED-NeRF achieves faster editing speed while producing improved output quality compared to state-of-the-art 3D editing models. Code and rendering results are available at our project page. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Distillation

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN) 7

Development of Detection Lung Disease Stand Alone System Bas...

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7th International Conference of Computer and Informatics Engineering, IC2IE 2024

作者： Liewarnata, Edgar Caesar Mariana, Yosica Tan, Sofyan Liawatimena, Suryadiputra Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Automotive and Robotics Program Computer Engineering Department Binus Aso School of Engineering Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification. © 2024 IEEE.

关键词： Lung cancer

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