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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Seleem, Ibrahim A. Naeem, Mohamed A. Ishii, Hiroyuki Waseda University Department of Modern Mechanical Engineering Japan Menoufia University Faculty of Electronic Engineering Department of Industrial Electronics and Control Engineering Egypt Harbin Institute of Technology School of Mechanical and Electrical Engineering Department of Mechatronics Engineering China South Valley University Faculty of Engineering Department of Mechatronics and Robotics Engineering Egypt

ISBN: (纸本)9798350325706

Extensible Soft Robots (ESRs) have increasingly attracted attention, especially in tight and complex environments, owing to their dexterity, and wide reachable workspaces relative to their volume. The existing actuation methods of ESR still suffer from challenges including large dimensions, expensive, complex modeling and control, and limited payload capacity. In this paper, we introduce a novel design for a lightweight, extensible and variable stiffness soft robot-based cable-driven actuation without backbone. The design composes of one section with three segments based on semi-octagon honeycomb structure, wherein the middle segment acts like a soft spring to provide extension feature. Additionally, the top and bottom segments have identical structure with two honeycomb patterns embedded within each other to improve the stiffness of the design. A design analysis is conducted to optimize the proposed structure with respect to stress and displacement. Additionally, a shape estimation approach is utilized to get accurate inference of the prototype's shape based on data acquired from a low-cost Inertial Measurement Unit (IMU) and Motion Capture System (MCS), and constant curvature assumption. A series of experiments including workspace reach-ability, repeatability, shape approximation and payload analysis are carried out to validate the proposed design. The results show that the prototype exhibits good compression/extension ratio up to 42% and 50% relative to its normal length, respectively. Moreover, its payload capability reaches to 565 grams. © 2023 IEEE.

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Analysis of the performance of robotic assembly systems considering the usage of tools or jigs for the task-board task in the WRS 2018

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Advanced robotics 2023年第20期37卷 1329-1346页

作者： Dobashi, Hiroki Ogawa, Koki Shibata, Mizuho Uemura, Wataru Yokokohji, Yasuyoshi Faculty of Systems Engineering Wakayama University Wakayama Japan Graduate School of Systems Engineering Wakayama University Wakayama Japan Department of Robotics Faculty of Engineering Kindai University Higashi-Hiroshima Japan Electronics Information and Communication Engineering Course Faculty of Advanced Science and Technology Ryukoku University Otsu Japan Department of Mechanical Engineering Graduate School of Engineering Kobe University Kobe Japan

A task-board task is a task in which robots perform a set of fundamental assembly operations related to the assembly of a specific product such as inserting a part into another part, tightening bolts and nuts, attaching flexible parts, etc. In the task-board task competition in the industrial robotics category of the World Robot Summit (WRS) 2018, 15 teams from around the world performed the task. In our previous work, we observed recorded videos of the competition frame by frame and manually investigated the performance of robotic systems of the top four teams, focusing only on whether robots contact target parts. In this paper, we introduce frame sets to represent mainly contact states between robots, tools, or jigs and target parts to be handled for such a frame-by-frame analysis of the recorded videos, and based on the frame sets, we analyze the performance of robotic assembly systems of other teams as well as the top four teams in the task-board task competition in the WRS 2018, considering the usage of tools or jigs. © 2023 Informa UK Limited, trading as Taylor & Francis Group and The robotics Society of Japan.

关键词： Robotic assembly

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Automation Lamp Healing Application based on Face Expression Identification System using Convolution Neural Network (CNN)

Automation Lamp Healing Application based on Face Expression...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Saduki Simangunsong, Devin Onil Mahesh Puji, Muhammad Nurul Astuti, Winda Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350381290

The disruption of routines, education, recreation, as well as concerns about family income and health, has left many young people feeling fearful, angry, and worried about their future. In this system, a smart system will be developed that can capture, process, and interpret facial expressions so that a person's emotions can be easily and accurately identified, which is one of the best solutions. The system will use artificial intelligence that has been trained beforehand using suitable datasets. In the processed of developing the system, Face image with the size of 48 x 48 x 1 became the input of the system, processes by Convolution layer using Relu activation function. The activation function used in the output layer is softmax because it is suitable for more than 2 classifications. by applying CNN based on Adam optimization which have result on 98.96% and 70%, for training and testing accuracy, respectively. Furthermore, the system accurately turn-on the healing lamp based on the emotion identified. © 2023 IEEE.

关键词： Face recognition

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A Brief Review of Traumatic Brain Injury Based on Finite Element Modeling

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Multiscale Science and engineering 2025年 1-10页

作者： Raouf, Izaz Sohail, Muhammad Kim, Heung Soo Chung, Jae-Hyun Department of Mechanical Robotics and Energy Engineering Dongguk University Seoul Republic of Korea Department of Mechanical Engineering University of Washington Seattle USA

Finite element modeling (FEM) has proved a decisive tool to investigate the complex biomechanics of traumatic brain injury (TBI). This review examines the application of FEM in preclinical TBI research, focusing on simulating human injury mechanisms. The evolution of rat brain FE models is traced from early simplified approaches to advanced, anatomically detailed simulations that employ high-resolution imaging and computational methods. Key modeling techniques (image acquisition, mesh generation, material characterization) and the inherent challenges (limited experimental data, scaling issues, computational constraints) are addressed. Further, current research applications are highlighted, including controlled cortical impact, blast injury, and neurological disease investigations. Finally, future directions are explored, like integrating machine learning and multi-scale modeling to enhance the accuracy and predictive power of FEM to understand and mitigate TBI.

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Emotion Recognition Based on Voice Using Combination of Long Short Term Memory (LSTM) and Recurrent Neural Network (RNN) for Automation Music Healing Application 1

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

作者： Trisyanto, Daryl Elangi Reynard, Michael Oey, Endra Astuti, Winda Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Stress has become a problem for many people experience in the world. It can affect in decrease in activity and productivity in daily life. Therefore, it is important to reduce or even heal this stress. One way to do that is by listening to appropriate music. Emotion recognition based on voice intonation has been developed in this work. The system has the ability to recognize the emotion of the speaker then automatically play the appropriate music based on emotion of the speaker. In this work, the combination of Long Short Term Memory (LSTM) and Recurrent Neural Network (RNN) approach for identifying emotion based on voice intonation of the speaker is proposed. The Mel Frequency Cepstral Coefficient (MFCC) is adopted as a feature extraction of the voice, which will used as input to the combination of Long Short Term Memory (LSTM) and Recurrent Neural Network (RNN)-based identifier. The performance of the proposed technique has been investigated, especially for multiclass classification and it is found to produce good accuracy within short duration training time. The accuracy of 99.8% and 62.5%, for training and testing accuracy respectively. Furthermore, the system accurately plays the music based on the emotion identified. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Long short-term memory

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Potentiodynamic study and microstructural evaluation of the corrosion behavior of mill scale and alumina reinforced aluminium metal matrix composites

Potentiodynamic study and microstructural evaluation of the ...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adediran, Adeolu A. Mosadomi, Oreofeoluwa A. Ikubanni, Peter P. Nana, Achile Ikumapayi, Omolayo Nwaeju, Cynthia C. Ojji, Immanuel O. Afolalu, Sunday A. Landmark University Department of Mechanical Engineering Kwara State Omu-Aran Nigeria Science University of Johannesburg Mechanical Engineering South Africa CSIR-National Metallurgical Laboratory Jamshedpur831007 India Afe Babalola University Department of Mechanical Engineering Ado-Ekiti Nigeria Nigeria Maritime University Department of Mechanical Engineering Delta State Okerenkoko Nigeria Demontfort University Department of Mechatronics and Robotics Leicester United Kingdom

ISBN: (纸本)9798350358155

The current study reports the corrosion behavior of aluminium metal matrix composites (AMMCs) reinforced with mill scale (MS) and alumina (Al2O3) in a simulated marine environment (3.5 % NaCl solution). Electrochemical technique and optical microscopy were used to evaluate the corrosion behavior of the composites developed at varying weight ratios of MS (0, 2, 4, 6, 8) and Al2O3 (0, 2, 4, 6, 8) respectively. From the results obtained, the average grain size of the composites after exposure to the marine environment varies significantly. Sample A showed an average grain size of 55.02 μm, B - 26.85 μm, C - 28.12 μm, D - 27.03 μm, E - 32.31 μm and F (control) - 37.02 μm. The contributory influence of the reinforcing materials is evident in the variation noted in the average grain sizes. Their presence acts as active sites for corrosion initiation. The oxidation of metal ions causes severe pitting with sample C having the optimum corrosion rate of 0.9532 mm/yr and sample D showing the least corrosion rate of 0.6188 mm/yr. The morphology of the corroded surfaces confirms the presence of pitting corrosion on the composites developed. © 2024 IEEE.

关键词： Pitting

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Development of Detection Kidney Function Stand Alone System and Design based on Oral Ammonia Levels 7

Development of Detection Kidney Function Stand Alone System ...

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

作者： William Hansen, Natasha Angeline Mariana, Yosica Sitepu, Elioenai Astuti, Winda Siswanto, Melinda 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 Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Biotechnology Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

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. © 2024 IEEE.

关键词： Support vector machines

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Analysis of Knocking Potential Based on Vibration on a Gasoline Engine for Pertalite and Pertamax Turbo Using Signal Processing Methods 1

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

作者： Qadri, Munzir Astuti, Winda Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Knocking is one of the problems in internal combustion engines with many influencing factors, one of which is the use of fuel octane values that are not in accordance with the recommended use or engine specifications. Knocking can lead to poor performance of an engine and shorten its lifetime. To prevent this from happening, a study was conducted on how the octane rating can affect the knocking of an engine by comparing two types of fuel that have different octane rating levels. The most appropriate signal processing method was also determined to carry out this research. The engine speed was predetermined in each experiment. The achieved results prove that the engine has the potential of knocking to occur when using Pertalite-type fuel, caused by misfires. Signal processing is carried out using the Fast Fourier Transform (FFT) and Low Pass Filter (LPF) methods to obtain clear data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Gasoline

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UAV Wheat Rust Detection based on FasterNet-YOLOv8

UAV Wheat Rust Detection based on FasterNet-YOLOv8

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Liu, Huaiyu Zhang, Yueyuan Liu, Shuguang Zhao, Mengyang Sun, Lining Soochow University School of Mechanical and Electrical Engineering Department of Automation Engineering Suzhou215137 China Soochow University Robotics and Microsystems Center School of Mechanical and Electrical Engineering Suzhou215137 China

ISBN: (纸本)9798350325706

This paper introduces a UAV-based wheat rust detection system employing deep learning techniques. To address the limitations of traditional wheat rust disease detection methods, such as time and labor-intensive processes, lack of real-time monitoring, and excessive pesticide usage, In this paper, the YOLOv8 algorithm, representing the forefront of object detection methods is chosen for UAV wheat rust detection. We train the model on a prepared dataset. Subsequently, we evaluated 5 models, including YOLOv5, and YOLOv8, at two different scales (n, s) based on average precision (mAP@.5), precision, recall, and FPS. The experimental outcomes establish the superiority of the enhanced FasterNet-YOLOv8 model over YOLOv8 and YOLOv5 across all metrics, encompassing detection accuracy and processing speed. These findings affirm the viability of our suggested model within the UAV wheat rust monitoring system and underscore the efficacy of the model enhancements. © 2023 IEEE.

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How Fast Can a Robotic Drummer Beat Using Dielectric Elastomer Actuators?

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IEEE robotics and Automation Letters 2024年第3期9卷 2638-2645页

作者： Wakle, Sudhir Lin, Tse-Han Huang, Shu Basu, Sumit Lau, Gih-Keong National Yang Ming Chiao Tung University International College of Semiconductor Technology Hsinchu30010 Taiwan Indian Institute of Technology Kanpur Mechanical Engineering Department Kanpur208016 India National Yang Ming Chiao Tung University Department of Graduate Degree Program of Robotics Hsinchu30010 Taiwan Industrial Technology Research Institute Mechanical and Mechatronics System Research Labs Zhudong 31057 Taiwan National Yang Ming Chiao Tung University Department of Mechanical Engineering International College of Semiconductor Technology Hsinchu30010 Taiwan

Fast drumming presents a speed challenge to many robotic arms. To simultaneously meet the needs for speed, stroke, and force, we proposed and tested a new double-saddle dielectric elastomer actuator (DEA) as the artificial biceps for driving a lightweight robotic drummer. This work finds that fast force induction is instrumental to the fast drumming by a DEA-driven drumstick. While a pure-shear series of DEA under a large pre-stretch and lateral reinforcement can generate a large isotonic stroke, it is not fast in isometric force induction. Instead, this work found a double-saddle DEA, which is a degenerated two-segment pure-shear DEA with a middle laterally buckled beam, managed to induce a faster isometric force at a 0.5 s down time constant when its ultimate actuation tapered due to substantial strain stiffening effect. As such, this double-saddle DEA-driven drummer suffered less from the dynamic stroke decrement than the 8-segment DEA did. A 4.4 g DEA managed to swing freely a 7.8 g drumstick up to nearly 60° at 6 kV and a maximum tip speed of up to 0.45 m/s. The drumming frequency upon 5.5 kV activation was up to 2-2.5 Hz. In comparison, an 8-segment pure-shear DEA-driven drummer failed to reach the drum when pulsed at 2 Hz. Interestingly, a slower stroke excited multiple drumbeats due to secondary bounces. To achieve a faster drumming to match a human drummer, it is foreseen that a harder dielectric elastomer material will help drive a faster soft robot. © 2016 IEEE.

关键词： Dielectric materials

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