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IEEE International Workshop on Intelligent robots and Systems (IROS)

作者： Daeheui Won Ho-Gil Lee Jin-Young Kim Moosung Choi Min-Sung Kang Control and Intelligence Research Group Division for Applied Robot Technology (DART) Korea Institute of Industrial and Technology Ansan South Korea

In this paper, a new wearable input device recognizing human hand-motion, a keyglove, is proposed. The glove can resolve the disadvantages of the conventional input devices and can be adapted to the mobile computing environment The device is applicable as the touch-typing input method for users' easy understanding and improvement of input speed. The hand-motion for touch-typewriting using QWERTY keyboard, which is most widely being used currently, is analyzed. The analyzed results show that the model with 21 DOF can be reduced to one with approximately 14 DOF. The hand-motion capture module using low price sensors is also developed for hand-motion recognition. In general, however, typists using the keyglove system can not exactly determine the key position of desired input letter because the virtual keyboard does not have the physical location of key layouts, and that causes many typewriting errors at a long typewriting time. Therefore, an algorithm is needed to cope with the problem and to reduce typewriting errors due to personal disparities such as hand shapes and typewriting habits. For this reason, the time-variant prediction algorithm is proposed to minimize typewriting errors, and the possibility of the developed keyglove system is also verified.

关键词： Humans Mobile computing Keyboards Computer interfaces Wearable computers Handwriting recognition Intelligent robots Speech recognition Intelligent control Service robots

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Software architecture of human-like robot

Software architecture of human-like robot

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SICE Annual Conference

作者： Tae-Geun Lee Kwang-Woong Yang Moo-Sung Choi Un-Chol Shin Intelligent Robot Platform Research Team SoluBot Inc. Ansan South Korea Division for Applied Robot Technology Korea Institute of Industrial and Technology Ansan South Korea

Software architectures have been suggested for a non-industrial robot up to the present, however a few developer who working in the robot field are applying those to manufactured products. Such software architecture of the robot is not using with several reasons in the field of a robot in spite of excellent advantages. This paper proposes using a device management of the software architecture, which has a hierarchical structure, in a human-like robot. Especially, we applied to the rent business of the android platform, EveR1; the compound word of eve and robot. The device may be real mechanical parts like a sensor, motor, and camera or virtual parts like a motor arbiter, motion generator, and priority maker. The virtual parts are software components which are used in operating the android platform. The device management exists between hardware and software of a robot, and makes the hardware compatibility for applied to applications in the high layer of software. It is compatible due to separating similar functions of each device which is managed in the device management, and providing same abstract interfaces. For instance, an application developed by using a motor driver of one corporation is exchanged for a motor drive of the other corporation with minimal code modification.

关键词： Software architecture Humanoid robots robot sensing systems Hardware Application software Manufactured products Mechanical sensors robot vision systems Cameras Motor drives

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A grasp strategy with the geometric centroid of a groped object shape derived from contact spots

A grasp strategy with the geometric centroid of a groped obj...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ji-Hun Bae Sung-Woo Park Doik Kim Moon-Hong Baeg Sang-Rok Oh Department of Applied Robot Technology Ansan Research and Development Center Korea Institute of Industrial and Technology Ansan South Korea Interaction and Robotics Research Center Korea Institute of Science and Technology Seoul South Korea

This paper proposes a strategy for grasp and manipulation of unknown objects. In order to derive force relations of fingers, the groped shape of soft fingers is introduced. The groped shape is not equal to the real object shape, but is tightly related to the force equilibrium of fingers. By considering contact forces of the groped shape, a simple control parameters can be derived. The manipulation of an object is easily accomplished by embedding the concept of virtual centroid into the grasp control to redistribute internal forces. With these concepts, an object can be easily translated, rotated, and manipulated by relocating fingers. The proposed method is verified with experiments.

关键词： Force Shape Grasping robots Friction Sensors Fingers

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Development of a human-like narrator robot system in EXPO

Development of a human-like narrator robot system in EXPO

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IEEE Conference on robotics, Automation and Mechatronics

作者： Ho Seok Ahn Dong-Wook Lee Bruce MacDonald Robotics Research Group The University of Auckland Auckland New Zealand Department of Applied Robot Technology Korea Institute of Industrial Technology Ansan Republic of Korea

This paper introduces a human-like narrator robot system called EveR-4E. EveR-4E is an android robot platform having human-like appearance, and explains exhibitions with facial expression and gestures. She has movable upper body and mannequin legs for standing on the floor. The skin of the head, arms, and hands is made of silicon jelly to give human-like texture. EveR-4E has fifty-nine degrees of freedom in the head, neck, arms, hands, and torso. Her gestures are designed based on captured body motions of human to give human-like movement. She can be controlled by remote systems controlled by human guide. She introduces robots in the robot exhibit hall of the EXPO 2012 Yeosu Korea.

关键词： Androids Humanoid robots Service robots Face Conferences Floors

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FBXO22 deficiency defines a pleiotropic syndrome of growth restriction and multi-system anomalies associated with a unique epigenetic signature

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American Journal of Human Genetics 2025年第5期112卷 1233-1246页

作者： Ramakrishna, Navin B. Mohamad Sahari, Umar Bin Johmura, Yoshikazu Ali, Nur Ain Alghamdi, Malak Bauer, Peter Khan, Suliman Ordoñez, Natalia Ferreira, Mariana Pinto Basto, Jorge Alkuraya, Fowzan S. Faqeih, Eissa Ali Mori, Mari Almontashiri, Naif A.M. Al Shamsi, Aisha ElGhazali, Gehad Abu Subieh, Hala Al Ojaimi, Mode El-Hattab, Ayman W. Said Al-Kindi, Said Ahmed Alhashmi, Nadia Alhabshan, Fahad Al Saman, Abdulaziz Tfayli, Hala Arabi, Mariam Khalifeh, Simone Taylor, Alan Alfadhel, Majid Jain, Ruchi Sinha, Shruti Shenbagam, Shruti Ramachandran, Revathy Altunoğlu, Umut Jacob, Anju Thalange, Nandu El Bejjani, Mireille Perrin, Arnaud Shin, Jay W. Al-Maawali, Almundher Al-Shidhani, Azza Al-Futaisi, Amna Rabea, Fatma Chekroun, Ikram Almarri, Mohamed A. Ohta, Tomohiko Nakanishi, Makoto Alsheikh-Ali, Alawi Ali, Fahad R. Bertoli-Avella, Aida M. Reversade, Bruno Abou Tayoun, Ahmad Genome Institute of Singapore (GIS) Agency for Science STAR) 60 Biopolis Street Genome Singapore 138672 Singapore Department of Biochemistry National University of Singapore Singapore 119260 Singapore Division of Cancer and Senescence Biology Cancer Research Institute Institute for Frontier Science Initiative Kanazawa University Kanazawa Japan Unit of Medical Genetics Department of Pediatrics College of Medicine King Saud University Medical City King Saud University Riyadh Saudi Arabia Centogene AG Rostock Germany Department of Translational Genomics Center for Genomic Medicine King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia Section of Medical Genetics King Fahad Medical City Children's Specialist Hospital Riyadh Saudi Arabia Department of Pediatrics The Ohio State University College of Medicine Columbus OH United States Genetic and Genomic Medicine Nationwide Children's Hospital Columbus OH United States Center for Genetics and Inherited Diseases Taibah University 7534 Abdul Muhsin Ibn Abdul Aziz Al Ihn Al-Madinah al-Munawwarah 42318 Saudi Arabia Faculty of Applied Medical Sciences Taibah University Janadah Bin Umayyah Road Tayba Al-Madinah al-Munawwarah 42353 Saudi Arabia Paediatrics Department Tawam Hospital Al-Ain United Arab Emirates HQ Medical Operations Division Union 71 Abu Dhabi United Arab Emirates Maternal Fetal Medicine Department Kanad Hospital Al Ain United Arab Emirates Department of Clinical Sciences College of Medicine University of Sharjah Sharjah United Arab Emirates Department of Neonatology Armed Forces Hospital Muscat Oman Child Health Department Royal Hospital Muscat Oman Department of Cardiac Sciences Ministry of National Guard Health Affairs Riyadh Saudi Arabia Pediatric Neurology Department National Neuroscience Institute King Fahad Medical City Riyadh Saudi Arabia Pediatric Endocrinology and Diabetes American University of Beirut Medical Center (AUBMC) Beirut Lebanon Department of Pe

FBXO22 encodes an F-box protein, which acts as a substrate-recognition component of the SKP1-CUL1-F-box (SCF) E3 ubiquitin ligase complex. Despite its known roles in the post-translational ubiquitination and degradation of specific substrates, including histone demethylases, the impact of FBXO22 on human development remains unknown. Here, we characterize a pleiotropic syndrome with prominent prenatal onset growth restriction and notable neurodevelopmental delay across 16 cases from 14 families. Through exome and genome sequencing, we identify four distinct homozygous FBXO22 variants with loss-of-function effects segregating with the disease: three predicted to lead to premature translation termination due to frameshift effects and a single-amino-acid-deletion variant, which, we show, impacts protein stability in vitro. We confirm that affected primary fibroblasts with a frameshift mutation are bereft of endogenous FBXO22 and show increased levels of the known substrate histone H3K9 demethylase KDM4B. Accordingly, we delineate a unique epigenetic signature for this disease in peripheral blood via long-read sequencing. Altogether, we identify and demonstrate that FBXO22 deficiency leads to a pleiotropic syndrome in humans, encompassing growth restriction and neurodevelopmental delay, the pathogenesis of which may be explained by broad chromatin alterations. © 2025 American Society of Human Genetics

关键词： DNA methylation E3 ubiquitin ligase epigenetics FBXO22 Mendelian disease neurodevelopmental disorder pleiotropic syndrome

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Real-Time Velocity and Hovering Control for Rotorcraft Based on Visual State Estimation

Real-Time Velocity and Hovering Control for Rotorcraft Based...

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2021 China Automation Congress, CAC 2021

作者： Du, Hao Wang, Wei Liu, Hongxun Wang, Qi Shi, Hongbiao Xue, Lei School of Automation Southeast University Nanjing China Nanjing University of Information Science & Technology Nanjing China Graduate School of Science and Engineering Chiba University Chiba Japan School of Automation Nanjing University of Information Science & Technology Nanjing China Autonomous Control Robot Laboratory Institute of Applied Research Intelligent Science & Technology Jiangsu and Chinese Academy of Sciences Changzhou China

ISBN: (纸本)9781665426473

The purpose of this thesis is to solve the velocity and hover flight control problem of rotorcraft in GNSS-denied environments by using a low-cost Optical Flow Sensor (OFS) and an RGBD camera. An OFS is employed for velocity estimation and an RGBD camera is employed for pose estimation based on ORB-SLAM2 algorithm. Firstly, the mathematical model of velocity is established, then the classical PID control is applied to realize the velocity and hover control, and the designed embedded control system is installed on the drone for flight verification. We verified the effectiveness of this system for estimating velocity, position and controlling the drone in real-time in an indoor environment, and discuss its limitations. © 2021 IEEE

关键词： Velocity

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Analysis of attention deficit hyperactivity disorder in EEG using wavelet transform and self organizing maps

Analysis of attention deficit hyperactivity disorder in EEG ...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Seung Hyun Lee Berdakh Abibullaev Won-Seok Kang Yunhee Shin Jinung An Pragmatic Applied Robot Institute Daegu-Gyeongbuk Institute of Science and Technology Daegu South Korea Research Institute of Special Education & Rehabilitation Science Daegu University Daegu South Korea

This paper presents our preliminary study EEG brain signals of children with attention deficit hyperactivity disorder (ADHD) in order to support a computer assisted diagnostic system. The EEG signals were recorded from 13 children including normal and children diagnosed with ADHD. We analyzed the signals with multilevel discrete wavelet decompositions in order to extract brain signal power spectrum features. A wavelet thresholding technique was employed to further improve the data quality by denoising the artifacts. In order to discriminate the attention level in electrical brain activity of ADHD children, we used a standard Self-Organizing Map clustering technique with wavelet coefficient input features. Clustering results varied depending on the wavelet feature extraction stage, particularly it was noticed that accuracy was dependent on the type of the used wavelet function. The clustering results demonstrate that `sym7' wavelet function provides better input feature localization to provide the accurate separation of normal and disordered children's brain activity.

关键词： Wavelet transforms Electroencephalography Feature extraction Pediatrics Accuracy Brain

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LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping

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Geoderma 2025年 459卷

作者： Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Department of Agromechatronics Potsdam Germany University of São Paulo (USP) Center of Nuclear Energy in Agriculture (CENA) Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany University of Bonn Institute of Crop Science and Resource Conservation (INRES)—Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Federal University of Santa Maria (UFSM) Academic Coordination Santa Maria Brazil Federal University of Santa Maria (UFSM) Soil Department Santa Maria Brazil Norwegian Institute of Bioeconomy Research (NIBIO) Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence ‘Machine Learning for Science’ Germany Bern Universi

Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number (<20) of features. These benchmarking results illustrate that the performance of statistical methods can be highly context-dependent. LimeSoDa therefore provides an important resource for improving the development and evaluation of statistical methods in DSM. © 2025 The Author(s)

关键词： Benchmarking Dataset collection Digital soil mapping Machine learning Open data Pedometrics

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LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping

arXiv

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arXiv 2025年

作者： Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Department of Agromechatronics Potsdam Germany Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Academic Coordination Santa Maria Brazil Soil Department Santa Maria Brazil Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence Machine Learning for Science’ Germany Bern University of Applied Sciences Competence Center for Soils Zollikofen Switzerland Simulation and Data Science Müncheberg Germany Federal University of Jataí Institute of Agricultural Sciences Jatai Brazil Federal University of Mato Grosso Instute of Agricultural and Environmental Scinces Sinop Brazil Department of Soil and Environment Skara

关键词： Adversarial machine learning

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Continuous and Embedded Learning for Multi-Agent Systems

Continuous and Embedded Learning for Multi-Agent Systems

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2006 IEEE/RSJ International Conference on Intelligent robots and Systems

作者： Zsolt Kira Alan C. Schultz Mobile Robot Laboratory Georgia Institute of Technology Atlanta GA USA Navy Center for Applied Research in A.I. Naval Research Laboratory Inc. Washington D.C. DC USA

This paper describes multi-agent strategies for applying continuous and embedded learning (CEL). In the CEL architecture, an agent maintains a simulator based on its current knowledge of the world and applies a learning algorithm that obtains its performance measure using this simulator. The simulator is updated to reflect changes in the environment or robot state that can be detected by a monitor, such as sensor failures. In this paper, we adapt this architecture to a multi-agent setting in which the monitor is communicated among the team members effectively creating a distributed monitor. The parameters of the current control algorithm (in our case rulebases learned by genetic algorithms) used by all of the agents are added to the monitor as well, allowing for cooperative learning. We show that communication of agent status (e.g. failures) among the team members allows the agents to dynamically adapt to team properties, in this case team size. Furthermore, we show that an agent is able to switch between specializing within a section of the domain when there are many team members and generalizing to other parts of the domain when the rest of the team members are disabled. Finally, we also discuss future potential of this method, most notably in the creation of a distributed case based reasoning system in which the cases are actual genetic algorithm population members that can be swapped among team members

关键词： Multiagent systems Genetic algorithms Condition monitoring Switches robot sensing systems Laboratories Communication switching Intelligent robots Mobile robots Current measurement

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