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作者： Kim, Jeongyun Yun, Seungsang Jung, Minwoo Kim, Ayoung Cho, Younggun Department of Civil and Environmental Engineering KAIST Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of Department of Robotics Engineering Yeungnam University Gyeongsan Korea Republic of

Recently, quantitative and repetitive inspections of the old urban area were conducted because many structures exceed their designed lifetime. The health of a building can be validated from the condition of the outer wall, while the slant angle of the wall widely serves as an indicator of urban regeneration projects. Mostly, the inspector directly measures the inclination of the wall or partially uses 3D point measurements using a static light detection and ranging (LiDAR). These approaches are costly, time-consuming, and only limited space can be measured. Therefore, we propose a mobile mapping system and automatic slant map generation algorithm, configured to capture urban environments online. Additionally, we use the LiDAR-inertial mapping algorithm to construct raw point clouds with gravity information. The proposed method extracts walls from raw point clouds and measures the slant angle of walls accurately. The generated slant angle map is evaluated in indoor and outdoor environments, and the accuracy is compared with real tiltmeter measurements. 1225-6463/$ © 2021 ETRI

关键词： Conformal mapping

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Development of Automatic Detection and Monitoring Application System for Peristaltic Condition based on Machine Learning and Internet of Thing (IoT) System

Development of Automatic Detection and Monitoring Applicatio...

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International Conference of Computer and Informatics engineering (IC2IE)

作者： Winda Astuti Giovanni Benedict Davin K Muhammad Nurul Puji Matthew Kunaifi Yuli Astuti Andriatin Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Cilacap Regional General Hospital Cilacap Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Poor sanitation and limited access to clean water are the main causes of increased risk of gastrointestinal diseases. In Indonesia, environmental hygiene problems are the main cause of digestive tract disorders, which contribute to more than 25% of deaths each year according to data from the Ministry of Health. Digestive disease is one of the diseases that often appears due to Poor sanitation and limited access to clean water. The development of a digital stethoscope for early detection of problems in the digestive tract would be useful, especially in remote areas. The sound was recorded from a Mel Frequency Cepstral Coefficient (MFCC) technique. The resulting features extracted from the stethoscope sound signal, are used as the inputs for the machine learning which applied three types of method, they are Logistic Regression (LR), combination of Logistic Regression (LR) and K-Nearest Neighbors (KNN), combination of Logistic Regression (LR) and Support Vector Machine (SVM). The experiment of MFCC with three types of machine learning methods above, based the sound from a stethoscope. Experimental results show the combination of Logistic Regression (LR) and K-Nearest Neighbors (KNN) has exceeded results among other methods, with the training and testing accuracy of 90 % and 88 % respectively. They confirm the effectiveness of the proposed intelligent automatic stethoscope sound identification based on Mel-Frequency Cepstral Coefficient (MFCC) and Linear Regression. Furthermore, the system continuously saves and monitors the result of the patient.

关键词： Support vector machines Training Logistic regression Accuracy Stethoscope Machine learning Nearest neighbor methods Mel frequency cepstral coefficient Diseases Testing

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Quantitative Analysis of Ankle Spasticity Through Electromyography Signal

Quantitative Analysis of Ankle Spasticity Through Electromyo...

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Medical Technologies National Conference (TIPTEKNO)

作者： Ayse Gurluk Hafiz Muhammad Bilal Elif Hocaoglu Living Robotics Laboratory Graduate School of Health Sciences Physiotherapy and Rehabilitation Program Istanbul Medipol University Istanbul Türkiye Living Robotics Laboratory Graduate School of Engineering and Natural Sciences Electrical-Electronics Engineering and Cyber-Systems Program Istanbul Medipol University Istanbul Türkiye Department of Electrical and Electronics Engineering Living Robotics Laboratory Istanbul Medipol University Istanbul Türkiye

ISBN: (数字)9798331529819

ISBN: (纸本)9798331529826

This study presents a more accurate evaluation method for assessing ankle spasticity compared to conventional goniometric techniques by utilizing measurements obtained from Inertial Measurement Units (IMU) and surface electromyography (sEMG) devices. Integrating IMU and EMG sensors provides substantial benefits for accurately determining the range of motion (ROM) associated with spasticity during targeted movements. In this study, the proposed method provides precise measurement and an objective evaluation of onset time detection of antagonistic muscle co-contraction, and accordingly the ROM of the actuated joint. Moreover, the implementation of cost-effective wearable sensors enables the monitoring of muscle spasticity in clinical environments and can be particularly valuable in regions with limited access to experienced therapists, providing a more efficient approach to tracking spasticity levels.

关键词： Ankle Measurement units Inertial navigation Muscles Electromyography Sensors Reliability Motion control Read only memory Monitoring

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Development of Detection Kidney Function Stand Alone System and Design based on Oral Ammonia Levels

Development of Detection Kidney Function Stand Alone System ...

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International Conference of Computer and Informatics engineering (IC2IE)

作者： William Natasha Angeline Hansen Yosica Mariana Elioenai Sitepu Winda Astuti Melinda Siswanto Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Product Design Program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Biotechnology Engineering Department Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

The development of detection kidney function system at the first prototype have problem with the dependence on the internet, which make not practical in the development of the system and the initial design was not ergonomic. In this work the development of the second step is done with the improvement of accuracy compared with other methods. In this work the new design of the embedded system is also applied and in term of algorithm, the system applied for woman patient. Three different types of machine learning techniques, namely K-nearest neighbors (KNN), Decision Tree and Random Forest are applied and compared. The AI-based kidney failure severity identification system with KNN algorithm had an average accuracy of 90% and 91% for training and testing accuracy, respectively. The design of the embedded system also renews with stylist, simple and practical.

关键词： Training Accuracy Machine learning algorithms Embedded systems Nearest neighbor methods Vectors Decision trees Kidney Random forests Testing

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Smart Shopping and Cart Billing System Using Deep Learning

Smart Shopping and Cart Billing System Using Deep Learning

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Ramkumar S Saravanan R Kanagaraj Venusamy Rani Jabbar N. Jeevitha Department of Robotics and Automation Rajalakshmi Engineering College Thandalam Chennai India Department of Mechatronics Engineering Rajalakshmi Engineering College Chennai India EDICT Department Electrical Engineering Program Bahrain polytechnic Isa town Kingdom of Bahrain Department of Electrical and Electronics Engineering Roever Engineering College Perambalur India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

There are many problems faced by people who stand for hours at billing counter after shopping, even though crowds are avoided everywhere, shopping mall billing queue isn't reducing since ages. Since the world relies on automation today, everyone has to automate processes which consumes time, money and efforts wherever possible. The technique related with the use of Raspberry Pi, LCD display and Webcam helps the people to bill the groceries within the shopping cart. It reduces consumption of time and their energy can be conserved which can't happen when they stand in a queue for billing process. User have to scan the object in front of webcam and LCD display allows them to add purchased items along with their price and quantity. They can also remove the product fromthe cart and it will be automatically removed. At the end of process, payment can be made online using a QR code or UPI. The Bill will be generated to their respective mail or it will be sent as SMS. User have to register or Log in when they start shopping. Automatic detection is possible without any inspection behind. The model is much portable and does not face any problem in its operation. When compared with the existing methods the processor, display along with its webcam accuracy is much better, it does not require complete continuous guidance and maintenance upon its usage. The need to push the cart has been eliminated as the cart can move along/follow the user autonomously.

关键词： Deep learning Webcams QR codes Inspection Liquid crystal displays Registers Maintenance Internet of Things Faces Postal services

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Development of a Lightweight Real-Time Application for Dynamic Hand Gesture Recognition

Development of a Lightweight Real-Time Application for Dynam...

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IEEE International Conference on Mechatronics and Automation

作者： Oluwaleke Yusuf Maki Habib Department of Mechanical Engineering Robotics Control and Smart Systems (RCSS) Program The American University in Cairo (AUC) Cairo Egypt

Hand Gesture Recognition (HGR) is a form of perceptual computing with applications in human-machine interaction, virtual/augmented reality, and human behavior analysis. Within the HGR domain, several frameworks have been developed with different combinations of input modalities and network architectures with varying levels of efficacy. Such frameworks maximized performance at the expense of increased hardware and computational requirements. These drawbacks can be tackled by transforming the relatively complex dynamic hand gesture recognition task into a simpler image classification task. This paper presents a skeleton-based HGR framework that implements data-level fusion for encoding spatiotemporal information from dynamic gestures into static representational images. Said static images are subsequently processed by a custom, end-to-end trainable multi-stream CNN architecture for gesture classification. Our framework reduces the hardware and computational requirements of the HGR task while remaining competitive with the state-of-the-art on the CNR, FPHA, LMDHG, SHREC2017, and DHG142S benchmark datasets. We demonstrated the practical utility of our framework by creating a lightweight real-time application that makes use of skeleton data extracted from RGB video streams captured by a standard inbuilt PC webcam. The application operates successfully with minimal CPU and RAM footprint while achieving 93.46% classification accuracy with approximately 2s latency at 15 frames per second.

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Unified Editing of Panorama, 3D Scenes, and Videos Through Disentangled Self-Attention Injection

arXiv

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arXiv 2024年

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

While text-to-image models have achieved impressive capabilities in image generation and editing, their application across various modalities often necessitates training separate models. Inspired by existing method of single image editing with self attention injection and video editing with shared attention, we propose a novel unified editing framework that combines the strengths of both approaches by utilizing only a basic 2D image text-to-image (T2I) diffusion model. Specifically, we design a sampling method that facilitates editing consecutive images while maintaining semantic consistency utilizing shared self-attention features during both reference and consecutive image sampling processes. Experimental results confirm that our method enables editing across diverse modalities including 3D scenes, videos, and panorama images. © 2024, CC BY.

关键词： Semantics

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

arXiv

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arXiv 2023年

作者： Park, Jangho Kwon, Gihyun Ye, Jong Chul Kim Jaechul Graduate School of AI Korea Republic of Robotics Program 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 page1 © 2023, CC BY.

关键词： Distillation

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Pose Synchronization for Semi-Autonomous Dynamic Robotic Swarms with a Passivity-Short Human Operator

Pose Synchronization for Semi-Autonomous Dynamic Robotic Swa...

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Annual Conference of Industrial Electronics Society

作者： Reo Kanazawa Toshiyuki Murao Hiroyuki Kawai Graduate Program in Mechanical Engineering Kanazawa Institute of Technology Ishikawa Japan Department of Robotics Kanazawa Institute of Technology Ishikawa Japan

This paper presents a pose synchronization method for semi-autonomous dynamic robotic swarms with a passivityshort human operator. In the human-robot network, a human operator sends a command signal to and receives visual information from only a part of the robotic group. In this setting, the proposed dynamic control scheme achieves not only pose synchronization of all mobile robots but also convergence to the reference pose that the human operator specifies. The main advantage of the proposed method is to guarantee convergence of not only the position but also the orientation to the reference ones in the human-robot network. Finally, we provide human-in-the-loop simulation results through the human operation using a tablet PC to illustrate the performance of the proposed control method.

关键词：

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Analytical reference compensation for tracking dynamic target signals with linear robust MPC strategies

Analytical reference compensation for tracking dynamic targe...

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American Control Conference (ACC)

作者： Tito L. M. Santos Bruno S. S. Pereira Department of Electrical and Computer Engineering Federal University of Bahia Salvador Bahia Brazil Graduate Program on Electrical and Engineering Federal University of Bahia Salvador Bahia Brazil Department of Robotics SENAI-CIMATEC Salvador Bahia Brazil

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper proposes an analytical target modifi-cation for linear robust model predictive control strategies in order to deal with time-varying references defined by dynamic signal targets. The new approach can be directly integrated to linear robust model predictive control algorithms that achieve piecewise constant reference tracking if recursive feasibility is ensured for any set-point. The main contribution is to present a direct analytical approach that provides a potentially improved steady-state tracking error performance with the same computation complexity of the original MPC for tracking piecewise constant reference. A simulation case study based on the trajectory tracking control of a quadrotor is used to illustrate the usefulness of the new analytical target modification layer.

关键词： Sufficient conditions Target tracking Trajectory tracking Heuristic algorithms Prediction algorithms Steady-state Computational complexity

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