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Towards understanding the surface rippling process by periodic reciprocal nanoscratching

Friction 2023年第10期11卷 1815-1828页

作者： Zihan LI Yongda YAN Jiqiang WANG Chen LI Yanquan GENG State Key Laboratory of Robotics and Systems Robotics InstituteHarbin Institute of TechnologyHarbin 150080China Center for Precision Engineering Harbin Institute of TechnologyHarbin 150001China

The bundle structure formed perpendicular to the scratching direction is a type of wear-induced structure for *** this study,the formation mechanism of bundle structures on polycarbonate(PC)surfaces is investigated by reciprocal scratching *** on the analysis of the morphologies,friction forces,and height signals,the formation of the bundle structure is *** influence of scratching parameters,including the feed value and scratching direction,on the formation of the bundle structure is also *** is found that the bundle structure is accumulated by the continuous stacking of the sample materials plowed by the tip in stick–slip motion,and that the stick–slip behavior is enhanced with increased scratching *** work reproduces the formation process of bundle structure in experiments for the first time and demonstrates that the stick–slip enhancement mechanism exists in the reciprocal scratching process,providing further insight into the friction behavior of polymers.

关键词： atomic force microscopy(AFM) bundle structures stick-slip friction force

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Photostriction effect and electric properties of La-doped PMN-PT transparent ferroelectric ceramics

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International Journal of Smart and Nano Materials 2024年第2期15卷 296-311页

作者： Zhiqiang Song Zhuo Zhang Yirong Yu Dabin Lin Department of Automation and Robotics Engineering School of AutomationWuxi UniversityWuxiChina School of Optoelectronic Engineering Xian Technological UniversityXi'anChina

Ferroelectric ceramics display a remarkable photostriction effect,which could facilitate the prevention of electromagnetic interference and promote the integration and miniaturization of actuator systems,indicating potential applications in glim-mer *** this work,highly transparent La-doped 0.9Pb(Mg1/3Nb2/3)O3-0.1PbTiO3(0.9PMN-0.1PT)ceramicswere synthesized using a solid-state reaction method in an oxyge-nated *** and SEM were used to analyze the phase structure of the dense samples,which displayed a pure perovskite structure,with no trace of a pyrochlore *** La doping,the phase transitiontemperature shows a decreasing trend,and its dielectric constant could reach up to 20,*** P-E loop of the samples was gradually refined into a straight line with an increase in the doping *** La doping,the transparency of the 0.9PMN-0.1PT cera-mics was significantly enhanced and reached up to 61%at the near-infrared ***,the photogenerated voltage and deformation of the La-doped 0.9PMN-0.1PT ceramics were investigated under ultraviolet light of various *** 20 s of irradiation at an intensity of 800 mW/cm²,the max-imum voltage and deformation,with values of 40 V and 250 nm,respectively,were both found in the 1%La-doped 0.9PMN-0.1PT ceramics.

关键词： Photostriction ferroelectric Raman piezoelectric

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Automatic Cataract and Cholesterol Detection System Based on Recurrent Neural Network (RNN): A Comparison with Convolutional Neural Network (CNN) and Siamese Neural Network (SNN) 1

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19th International Conference of Intelligent Unmanned Systems, ICIUS 2023

作者： Lie, Zener S. Widodo, Jatmiko A. F. Hanesi, Isabel K. P. Moningka, Matthew R. N. Mariana, Y. Astuti, Winda Akmeliawati, Rini Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Product Design program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Robotics and Automation Research Group School of Electrical and Mechanical Engineering The University of Adelaide North Terrace Campus Adelaide Australia

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

In Indonesia, the majority of blindness cases, approximately 81%, are attributed to cataract. Additionally, 35% of the population has higher cholesterol levels than the standard normal range. The lack of specialized medical equipment, such as the slit-lamp (Ruparelia in Dalhousie Med J 49(1), [1]) bio-microscope, and trained professionals, especially in rural areas, make it difficult to detect these conditions through eye examinations. To address this issue, a supervised machine learning method, the Recurrent Neural Network (RNN), has been utilized to create an iris scanner system that can identify cataract and high cholesterol in a patient. This system will classify eyes into three categories, i.e., cataract, high cholesterol, and normal iris. The computer simulations indicate that this technique is more accurate and requires less training time compared to the existing Convolutional Neural Network (ANN) and Siamese Neural Network (SNN)-based method, while achieving 100% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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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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WareOdo: Leveraging warehouse landmarks for correcting odometry in indoor mobile robots

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Intelligent Service robotics 2025年第3期18卷 687-708页

作者： Prasad, Srividya D. Srinivas, Siddharth Sulaiman, Syed Vaibhavi, M. Balakrishnan, Arjun Tripathi, Shikha Dept. of Electronics and Communication Engineering PES University Bengaluru India Accio Robotics Bengaluru India

The increasing use of robots in warehouse automation necessitates robust, cost-effective localization solutions. This work introduces a low-cost odometry system for indoor applications that maps common fiducial markers and lane markings, delivering precise robot pose corrections to an odometry source derived via an Extended Kalman Filter. The system utilizes a monocular camera, IMU, and wheel encoders as sensory inputs. It addresses the initialization challenges faced by existing visual-inertial-wheel algorithms, where limited initial movement hinders accurate estimates, and provides real-time pose corrections wherever landmarks are available, rather than waiting for loop closure, presenting a better correction strategy than post-processed pose graph optimization. The approach effectively eliminates error accumulation and mitigates incorrect z-axis estimation previously encountered for ground robots. Moreover, by promptly recovering localization upon observation of landmarks and ensuring robust tracking even in repetitive, low-texture environments, it inherently estimates odometry that can be used for better localization to overcome challenges like sudden pose loss and feature scarcity. Additionally, it features a fallback method to enhance odometry using lanes, even without the need for creating a lane map. Extensive experiments conducted with a wheeled mobile robot in complex warehouse environments-characterized by curvy trajectories, variable lighting, and multiple loops-and in simulation, demonstrate that the proposed system provides globally accurate, reliable, and drift-free odometry, unlike existing methods in large and visually challenging settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Mobile robots

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Computer Vision-Based Hydroponic Lettuce Contour and Area Recognition System

Computer Vision-Based Hydroponic Lettuce Contour and Area Re...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Pranowo, Samuel Raymond Sukra, Zener Lie Asy'ari, Muhammad Zacky Paryasto, Marisa Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Telkom University Computer Engineering Program Electrical Engineering Department Bandung Indonesia

ISBN: (纸本)9798350381290

Agriculture is a vital industry for the people of Indonesia, but there are several obstacles, including limited space in urban areas and inefficient conventional sorting and harvesting methods. Using computer vision to choose hydroponic lettuce that is ready to be harvested and a robotic arm to bring the selected lettuce yields, the produced system aims to solve this problem. The system's computer vision algorithms include color space conversion, intensity transformation, and an algorithm for following borders. This project's research methodology combines a quantitative approach with an experimental approach, which consists of conducting several trials on the built system. Several trials demonstrated that the computer vision software was able to accomplish the specified objectives. The average success rate of the developed computer vision system is 93%, while the average success rate of the robot arm is 85%. © 2023 IEEE.

关键词： Lettuce

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Continuous sign language recognition using intra-inter gloss attention

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Multimedia Tools and Applications 2025年 1-19页

作者： Ranjbar, Hossein Taheri, Alireza Social and Cognitive Robotics Lab. Mechanical Engineering Department Sharif University of Technology Tehran Iran

Many continuous sign language recognition (CSLR) studies adopt transformer-based architectures for sequence modeling due to their powerful capacity for capturing global contexts. Nevertheless, vanilla self-attention, which serves as the core module of the transformer, performs context-aware weighted aggregation over all time steps. Therefore, the local temporal semantics of sign videos may not be fully exploited. This study introduces a novel module for sign language recognition, the intra-inter gloss attention module, designed to leverage relationships among frames within glosses and to capture the semantic and grammatical dependencies between glosses in the video. In the intra-gloss attention module, the video is divided into equally sized chunks, and a self-attention mechanism is applied within each chunk. This localized self-attention significantly reduces complexity and eliminates noise introduced by considering non-relative frames, and achieves a 0.6 improvement in WER based on the baseline model while increasing inference speed by 20%. In the inter-gloss attention module, the chunk-level features within each gloss chunk are initially aggregated by average pooling along the temporal dimension, which leads to a 0.7 improvement in WER. Subsequently, multi-head self-attention is applied to all chunk-level features. Given the non-significance of the signer-environment interaction, we utilize segmentation module to remove the background of the videos. This enables the proposed model to direct its focus toward the signer and achieve an additional 1.5 improvement in WER. Experimental results on the PHOENIX-2014 benchmark dataset demonstrate that our method can effectively extract sign language features in an end-to-end manner without any prior knowledge, improve the accuracy of CSLR, and achieve the word error rate (WER) of 20.4 on the test set which is a competitive result compare to the state-of-the-art which uses additional supervisions. © The Author(s), under exc

关键词： Semantics

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AUGMENTED REALITY FOR KNOWLEDGE DISCOVERY THROUGH FEATURE DETECTION AND SEMANTIC SIMILARITY

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UPB Scientific Bulletin, Series D: Mechanical engineering 2025年第1期87 2025卷 139-148页

作者： Kleiman, Igor Gheorghe, Marian Nițu, Eduard Laurențiu Doctoral School of Industrial Engineering and Robotics POLITEHNICA Bucharest Romania OKO Suisse Kleiman Switzerland Manufacturing Engineering Dept Doctoral School of Industrial Engineering and Robotics POLITEHNICA Bucharest Romania Manufacturing and Industrial Management Dept. Doctoral School of Industrial Engineering POLITEHNICA Bucharest Pitești University Centre Romania

The need for real-time, context-aware knowledge discovery in AR environments led to a new approach that combines feature-based object recognition and semantic similarity search—an innovation beyond current systems. This method uses feature extraction to construct feature graphs, which are matched to knowledge products within knowledge bases. Based on object recognition and contextual cues, relevant knowledge products are retrieved and overlaid on physical objects via Augmented Reality Markup Language. This integration empowers users to access context-specific knowledge in real-time, enhancing decision-making and efficiency. In the context of industrial robotics, it shows substantial improvements in knowledge retrieval and task performance. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Markup languages

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Design and Analysis of a Novel Sch?nflies Motion Parallel Robot with Full Cycle Rotation

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Chinese Journal of Mechanical engineering 2024年第6期37卷 441-455页

作者： Luquan Li Yuefa Fang Lin Wang Jiaqiang Yao Institute of AI and Robotics Academy for Engineering and TechnologyFudan UniversityShanghai 200433China School of Mechanical Electronic and Control EngineeringBeijing Jiaotong UniversityBeijing 100044China

Due to the complex structures of multi-limbed parallel robots,conventional parallel robots generally have limited workspace,complex kinematics,and complex dynamics,which increases the application difficulty of parallel robot in industrial *** solve the above problems,this paper proposes a single-loop Sch?nflies motion parallel robot with full cycle rotation,the robot can generate Sch?nflies motion by the most simplified *** novel Sch?nflies motion parallel robot is a two-limb parallel mechanism with least links and joints,and each limb is driven by a 2-degree of freedom(DOF)cylindrical driver(C-driver).The full cycle rotation of the output link is achieved by“…R-H…”structure,where the revolute(R)and helical(H)joints are ***,kinematics,workspace and singularity analysis of the novel Sch?nflies motion parallel robot are ***,dynamic model is formulated based on the principle of virtual ***,a pick-and-place task is implemented by the proposed Sch?nflies motion parallel robot and a serial SCARA robot,*** simulation results verify the correctness of the theoretical ***,dynamics performances of the proposed Sch?nflies motion parallel robot and a serial SCARA robot are compared,which reveal the performance merits of the proposed Sch?nflies motion parallel robot.

关键词： Single-loop parallel robot Schönflies motion Virtual work principle Dynamics

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Analyze and Predict Car Accidents Using Different Machine Learning Algorithms 8

Analyze and Predict Car Accidents Using Different Machine Le...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Harimanto, Farrell Putra Andrew, Chris William, Henry Asy'ari, Muhammad Zacky Artanto, Samuel Albert Puji, Muhammad Nurul Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Traffic accidents can occur from various factors, ranging from the driver themselves to problems with the vehicle. Road and traffic accidents are the most significant factor of fatalities in the world. Most of the causes of accidents are the drivers themselves. Poor physical and mental conditions can significantly impact how to drive. The driving experience is also important;the longer the driver has been driving, the more professional he will be, and vice versa. Smart City solutions can predict the severity of accidents and give information to the authorities in order to reduce the number of fatalities. This paper investigates the factors that may lead to an accident on the road. By looking at data collected from traffic-related accidents and then visualizing it to make it easy to analyze;then, from this data, we will predict the causes of traffic accidents so they can be avoided. The best algorithm based on accuracy, MAE and RMSE is Random Forest Classifier with 0.8461, 0.1663 and 0.4376, respectively. © 2023 IEEE.

关键词： Machine learning

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