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Corrigendum to "Prevalence and risk factors of internet gaming disorder and problematic internet use before and during the COVID-19 pandemic: A large online survey of Japanese adults" [J. Psychiatr. Res. 142 (2021) 218-225]

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Journal of psychiatric research 2022年 148卷 275-276页

作者： Taiki Oka Toshitaka Hamamura Yuka Miyake Nao Kobayashi Masaru Honjo Mitsuo Kawato Takatomi Kubo Toshinori Chiba The Department of Decoded Neurofeedback Computational Neuroscience Laboratories Advanced Telecommunications Research Institute International Kyoto Japan. Health-improving Behavior Change Laboratory Innovation Center KDDI Research Inc. Saitama Japan. Technology Strategy Department Technology Planning Division KDDI CORPORATION Tokyo Japan. The Department of Brain Robot Interface Computational Neuroscience Laboratories Advanced Telecommunications Research Institute International Kyoto Japan. The Department of Decoded Neurofeedback Computational Neuroscience Laboratories Advanced Telecommunications Research Institute International Kyoto Japan The Department of Psychiatry Self-Defense Forces Hanshin Hospital Kawanishi Japan The Department of Psychiatry Kobe University Graduate School of Medicine Kobe Japan. Electronic address: t.chiba0906@***.

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Context-Aware Network for Semi-Supervised Segmentation of 3d Left Atrium

SSRN

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SSRN 2022年

作者： Zhao, Chenji Xiang, Shun Cai, Zhaoxi Shen, Jun Li, Shuo Zhou, Shoujun Zhao, Di Su, Weihua Guo, Shijie Wang, Yuanquan Tianjin300401 China Department of Radiology Sun Yat-Sen Memorial Hospital Sun Yat-Sen University Guangzhou510120 China DIG of London Department of Medical Imaging Western University LondonN6A 4V2 Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute of Computing Technology The Chinese Academy of Sciences Beijing100190 China Hebei Key Laboratory of Robot Perception and Human-Robot Interaction HeBUT Tianjin300401 China

Accurate, robust and automatic segmentation of the left atrium (LA) in magnetic resonance images (MRI) is of great significance for studying the LA structure and facilitating the diagnosis and treatment of atrial fibrillation. Semi-supervised learning has attracted great attention in medical image segmentation, since it alleviates the heavy burden of annotating training data. In this paper, we propose a context-aware network called CA-Net for semi-supervised LA segmentation from 3D MRI. In the proposed CA-Net, a Trans-V module is coined from both Transformers and V-Net, which is able to learn contextual information in 3D MRI. In the training processing, the discriminator with attention mechanisms is introduced to calculate an adversarial loss so that a large amount of unlabeled data can be utilized. Experimental results on the Atrial Segmentation Challenge dataset show that the contextual information is helpful to extract more accurate atrial structures, and the proposed CA-Net achieves better performance than some SOTA semi-supervised networks. © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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An Untethered Soft robotic Gripper with Adjustable Grasping Modes and Force Feedback

An Untethered Soft Robotic Gripper with Adjustable Grasping ...

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IEEE International Conference on robotics and Biomimetics

作者： Yang Yang Honghui Zhu Jia Liu Yingtian Li Jianshu Zhou Tao Ren Yi Ren School of Automation Nanjing University of Information Science and Technology Nanjing Jiangsu China Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu China Advanced Manufacturing Lab Huawei Technologies Shenzhen China

ISBN: (纸本)9781665481106

In this paper, we propose a three-finger soft robotic gripper, which adopts an untethered and under-actuated design with simple structure and easy operation. The rotation of two servomotors is used to control the positions of the fingers, which provides the fingers with adjustable grasping modes and improves the adaptation of the gripper to objects with different shapes. A pressure sensor is integrated at the end of the finger to provide force feedback, which is used to detect the contact force when grasping the object and enables the gripper to provide the proper grasping force. The experimental section characterizes the bending of the soft robotic finger, and the gripper is integrated at the end of a collaborative robotic arm to demonstrate its adjustable grasping posture and force feedback function. The soft robotic gripper is expected to be used in a variety of scenarios, such as service robot, food industry or garbage sorting.

关键词： Service robots Shape Fingers Force feedback Force Grasping Soft robotics

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Interfacial Microstructure and Strengthening Mechanism of Dissimilar Laser Al/Steel Joint Via a Porous High Entropy Alloy Coating

SSRN

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SSRN 2023年

作者： Zheng, Min Yang, Jin Xu, Jiayi Jiang, Jiawei Zhang, Hua Oliveira, J.P. Lv, Xueqi Xue, Jing Li, Zhuguo School of Materials Science and Engineering Shanghai University of Engineering Science Shanghai201620 China School of Advanced Manufacturing Nanchang University Nanchang330031 China Shanghai Collaborative Innovation Center of Intelligent Manufacturing Robot Technology for Large Components Shanghai201620 China CENIMAT|i3N Department of Materials Science School of Science and Technology NOVA University Lisbon Caparica Portugal Shanghai Key Laboratory of Material Laser Processing and Modification Shanghai Jiao Tong University Shanghai200240 China

A novel porous high entropy alloy (HEA) coating was prepared on the steel surface by vacuum sintering. The coating was then used as a transition layer during dissimilar laser Al/steel joining. Compared with the uncoated laser joints, the liquid alloy spread and infiltrated into porous structure, the contact angle of the weld reduced from 65.8 ° to 56.7°, and the brazed width increased from 5.1 mm to 5.9 mm, which improved the wettability and spreadability of the molten filler wire on the substrate. In the case of uncoated steel, the fusion zone/steel interfacial microstructure consisted of laminated Al7.2Fe1.8Si and Fe(Al,Si)3, while it changed to a composite-like structure containing a soft HEA skeleton and hard IMCs which was composed of Al7.2Fe1.8Si, Al3Ni, and (Al,Si)2Cr. In addition, due to the sluggish diffusion effect of HEAs, a layer of gradient nanocrystalline composed of Al7.2Fe1.8Si was generated, which significantly strengthened the dissimilar laser joints with improved both the fracture load (~26.5%) and the displacement (~101.8%). The fracture mode changed from brittle to ductile failure when the porous HEA coating was applied, with fracture propagating through the HEA skeleton. This work provides a novel solution for the strengthening of hard-to-join dissimilar combinations. © 2023, The Authors. All rights reserved.

关键词： Ductile fracture

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Data-driven modeling the nonlinear backlash of steerable endoscope under a large deflection cannulation in ERCP surgery

Data-driven modeling the nonlinear backlash of steerable end...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Jiang, Wei Yu, Tao He, Xiao Yang, Yongming Wang, Zhidong Liu, Hao Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Minimally Invasive Surgical Robot Liaoning Province Shenyang110016 China Key Laboratory of Minimally Invasive Surgical Liaoning Province Shenyang110016 China Department of Advanced Robotics Chiba Institute of Technology Narashino Chiba275-0016 Japan

ISBN: (纸本)9781665436786

Unexpected nonlinear backlash performances of two degrees of freedom (DoFs) tendon sheath mechanism (TSM) have serious impacts on the precise cannulation in ERCP (Endoscopic Retrograde Cholangio-Pancreatography) surgery. We proposed to model backlash performance of two DoFs steerable endoscope by using LSTM (Long Short-Term Memory) network. Trajectory following and orientation tasks are performed after the backlash model has been considered into the control strategy. Experimental results show that the LSTM backlash model can accurately describe the non-linearities of backlash, and significantly reduce the positioning and orientation deviation of the steerable endoscope under large deflection. The proposed backlash modeling method can be extended to be applied to bronchial, urethral and other natural orifice interventional tasks that work under a large deflection. © 2021 IEEE.

关键词： Endoscopy

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An Estimation Model of Nasopharyngeal Specimen Collection Path for Non-face-to-face Automatic Sampling robot

An Estimation Model of Nasopharyngeal Specimen Collection Pa...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Yonghwan Moon Jihoon Kim Suhun Jung Sang Kyung Kim Jeongryul Kim Keri Kim School of Mechanical Engineering Korea University 145 Anam-ro Seongbuk-gu Seoul Korea Augmented Safety System with Intelligence Sensing Korea Institute of Science and Technology 5 Hwarang-ro 14-gil Seongbuk-gu Seoul Korea Artificial Intelligence and Robot Institute Korea Institute of Science and Technology 5 Hwarang-ro 14-gil Seongbuk-gu Seoul Korea Department of Mechanical Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon Korea Division of Bio-Medical Science and Technology University of Science and Technology 217 Gajeong-ro Yuseong-gu Daejeon Korea

Due to the worldwide spread of COVID-19, each government invests many human resources and money in screening tests. The spread of the virus has led to the development of robots that track the location of specimen collection or drive directly through master-slave devices by installing special equipment on patients’ noses to reduce the physical burden on medical staff and prevent infection during screening tests. Sampling robots proposed in previous studies have a rather complicated specimen collection process or make it impossible to collect specimens when the patient cannot wear special equipment. Therefore, we propose a deep learning-based model that predicts the nasopharyngeal specimen sampling path without additional equipment. The test bench for the collection of learning datasets was configured, and the nasopharyngeal specimen sampling path was expressed using an augmented reality marker to learn the estimated value. In addition, we add weight factors to the proposed model to compare the root mean square error of the direction vector.

关键词： COVID-19 Point cloud compression Nose Process control Predictive models Prediction algorithms Real-time systems

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Motion generation interface of ROS to PODO software framework for wheeled humanoid robot

arXiv

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arXiv 2020年

作者： Lee, Moonyoung Heo, Yujin Cho, Saihim Park, Hyunsub Oh, Jun-Ho Humanoid Robot Research Center Department of Mechanical Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of

This paper discusses the development of robot motion generation interface between a real-time software architecture and a non-real-time robot operating system. In order for robots to execute intelligent manipulation or navigation, close integration of high-level perception and low-level control is required. However, many available open-source perception modules are developed in ROS, which operates on Linux OS that don't guarantee RT performance. This can lead to non-deterministic responses and stability problems that can adversely affect robot control. As a result, many robotic systems devote RTOS for low-level motion control. Similarly, the humanoid robot platform developed at KAIST, Hubo, utilizes a custom real-time software framework called PODO. Although PODO provides easy interface for motion generation, it lacks interface to high-level frameworks such as ROS. As such, we present a new motion generation interface between ROS and PODO that enables users to generate motion trajectories through standard ROS messages while leveraging a real-time motion controller. With the proposed communication interface, we demonstrate series of manipulator tasks on the actual wheeled humanoid platform, M-Hubo. The overall communication interface responsiveness was at most 27 milliseconds. Copyright © 2020, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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A 50 Μm Acoustically Actuated Microchannel Enables Focusing of 50 Nm Particles and Extracellular Vesicles by a 14.9 Mhz Two-Dimensional Ultrasound Standing Wave

SSRN

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SSRN 2022年

作者： Lee, SangWook Tsuyama, Yoshiyuki Xu, Bin Hattori, Kazuki Baek, Seugho Yoshioka, Yusuke Kojima, Ryosuke Cho, Younghak Laurell, Thomas Kim, Soyoun Ota, Sadao Department of Molecular and Cellular Medicine Institute of Medical Science Tokyo Medical University Tokyo160-0023 Japan Research Center for Advanced Science and Technology The University of Tokyo Tokyo153-8904 Japan PCL Inc. Seoul05854 Korea Republic of Graduate School of Medicine The University of Tokyo Tokyo113-0033 Japan Saitama332-0012 Japan Department of Mechanical Design and Robot Engineering Seoul National University of Science & Technology Seoul01811 Korea Republic of Department of Biomedical Engineering Lund University Lund22363 Sweden Convergence Research Institute Korea University Seoul02841 Korea Republic of

The manipulation and focusing of submicron/nanometre-scale objects are becoming increasingly important in the fields of biology, chemistry, and materials science. Acoustofluidics provides for the high throughput and precise manipulation of cells and particles with simple equipment. However, application to submicron/nanoparticles is challenging due to the particle volume dependence of the acoustic radiation force and relatively larger effect of the viscous drag force on small objects. Here, we present an acoustofluidic device that can focus submicron/nanoparticles, bacterial cells, and extracellular vesicles (EVs) using a high-frequency two-dimensional acoustic standing wave. We fabricate 50 µm × 50 µm square cross-section channels as acoustic resonators and generate a 2-dimensional acoustic standing wave at an actuation frequency of 14.9 MHz. This configuration enables a strong acoustic radiation force towards the centre of the microchannel and a cross-section centred single-vortex stream that does not counteract the acoustic radiation force. Using this device, polystyrene beads ranging in diameter from 50 to 500 nm were focused toward the channel centre, where a focused bead stream width of less than 5 μm was achieved at a flow rate of 3 μL min −1 for 100 nm beads and 9 µL min -1 for 200 nm beads. Furthermore, bacterial cells were successfully focused at a flow rate of 40 µL min -1 , and the focusing of EVs was also demonstrated. Our acoustofluidic device can become a promising tool for a wide range of biological analyses targeting submicron cells, viruses, and Evs. © 2022, The Authors. All rights reserved.

关键词： Acoustic radiators

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