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Effect of Carbon Density at Grain Boundary on Delayed Fracture Properties of Martensitic Steels

Zairyo/Journal of the Society of Materials science, Japan 2024年第2期73卷 109-114页

作者： Ichiro, Fujimoto Tatsuya, Okayama Ryosuke, Matsumoto Honda R&D Co. Ltd Haga-machi Haga-gun Tochigi 321-3393 Japan Department of Mechanical and Electrical Systems Engineering Faculty of Engineering Kyoto University of Advanced Science Yamanouchi Gotanda-cho Ukyoku Kyoto615-8577 Japan

It is well known that carbon addition enhances the strength of steel, but it also increases the sensitivity to delayed fracture. The origin of increased sensitivity to delayed fracture at a higher carbon content is not clarified yet. In this report, high-strength steels with different carbon contents were fabricated, and their delayed fracture properties were firstly evaluated. As the amount of carbon in the material increased, the amount of carbon concentration at the grain boundaries and the dislocation density also increased, and the material became more sensitive to delayed fracture. Then, a grain boundary (GB) model was created based on the samples used in the experiments, and the hydrogen effect on the cohesive energy of GB was evaluated using first principles calculations. The cohesive energy without carbon atoms was 3.47 J/m2 in the presence of hydrogen, while it with one additional carbon atom was 3.70 J/m2. This result was inconsistent to the experiment. Therefore, based on the results of dislocation density measurements, we assumed that more vacancies are generated in materials with a higher carbon content. By adding vacancy at GB, the cohesive energy decreased to 1.79 J/m2, and showed the same tendency as in the experiment. This suggests that not only hydrogen and carbon, but also vacancies, are responsible for the decrease in delayed fracture resistance. ©2024 The Society of Materials science, Japan.

关键词： Grain boundaries

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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Autonomous Exploration System of Electrical Stimulus Condition for Maturating in vitro Skeletal Muscle Tissue with Bayesian Optimization

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IEEJ Transactions on Sensors and Micromachines 2025年第3期145卷 33-40页

作者： Miyata, Daiki Kasahara, Keitaro Taguchi, Yujin Tokuoka, Yuta Yamada, Takahiro G. Kurashina, Yuta Funahashi, Akira Onoe, Hiroaki Department of Mechanical Engineering Faculty of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-0061 Japan Department of Biosciences and Informatics Faculty of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-0061 Japan Division of Advanced Mechanical Systems Engineering Institute of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Koganei Tokyo184-8588 Japan

This paper describes an autonomous exploration system to optimize electrical stimulation parameters for the maturation of in vitro skeletal muscle tissues with Bayesian optimization. We established a cyclic experimental procedures, where (i) C2C12 myoblasts were cultured under multiple electrical stimulus conditions in parallel to promote the maturation, (ii) the cell maturation levels were scored by microscopic image analysis with immunofluorescence staining, and (iii) each maturation score with electrical stimulus condition were then applied to Bayesian optimization to suggest new experimental stimulus conditions for the next cycle of the C2C12 culture to expect higher maturation scores. We performed this experimental cycle repeatedly and derived the trend of electrical stimulus conditions to obtain high maturation scores. The C2C12 myoblasts cultured under the derived condition were also verified by the increase of myogenic gene expression. We believe that our proposed method could reveal optimal application method for tissue maturation, and would contribute to regenerative medicine or other medical technologies using tissue culture. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Regenerative medicine

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Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

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Computers, Materials & Continua 2022年第8期72卷 2467-2482页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Shady Y.El-Mashad Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Mechanical Engineering Department Qassim UniversityBuraidah51452Saudi Arabia Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt

The sample’s hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing ***(HGB)is a critical component of the human body because it transports oxygen from the lungs to the body’s tissues and returns carbon dioxide from the tissues to the *** the HGB level is a critical step in any blood analysis *** often indicate whether a person is anemic or polycythemia *** ensemble models by combining two or more base machine learning(ML)models can help create a more improved *** purpose of this work is to present a weighted average ensemble model for predicting hemoglobin *** optimization method is utilized to get the ensemble’s optimum *** optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search(SCSFS).The proposed SCSFS ensemble is compared toDecision Tree,Multilayer perceptron(MLP),Support Vector Regression(SVR)and Random Forest Regressors as model-based approaches and the average ensemble *** SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.

关键词： Sine cosine optimization metaheuristics optimization hemoglobin estimation weight average ensemble

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Deep Forest-Based Fall Detection in Internet of Medical Things Environment

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Computer systems science & engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

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A piezoresistive-based 3-axial MEMS tactile sensor and integrated surgical forceps for gastrointestinal endoscopic minimally invasive surgery

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Microsystems & Nanoengineering 2024年第5期10卷 71-83页

作者： Cheng Hou Huxin Gao Xiaoxiao Yang Guangming Xue Xiuli Zuo Yanqing Li Dongsheng Li Bo Lu Hongliang Ren Huicong Liu Lining Sun School of Mechanical and Electrical Engineering Jiangsu Provincial Key Laboratory of Advanced RoboticsSoochow UniversitySuzhouChina Faculty of Mechanical and Electrical Engineering Kunming University of Science and TechnologyJingming South RoadKunmingChina Yunnan Key Laboratory of Intelligent Control and Application KunmingChina Department of Electronic Engineering The Chinese University of Hong Kong(CUHK)Hong KongChina. Department of Gastroenterology Qilu HospitalShandong UniversityJinanShandongChina

In robotic-assisted surgery(RAS),traditional surgical instruments without sensing capability cannot perceive accurate operational forces during the task,and such drawbacks can be largely intensified when sophisticated tasks involving flexible and slender arms with small end-effectors,such as in gastrointestinal endoscopic surgery(GES).In this study,we propose a microelectromechanical system(MEMS)piezoresistive 3-axial tactile sensor for GES forceps,which can intuitively provide surgeons with online force feedback during robotic *** MEMS fabrication process facilitates sensor chips with miniaturized *** fully encapsulated tactile sensors can be effortlessly integrated into miniature GES forceps,which feature a slender diameter of just 3.5 mm and undergo meticulous calibration procedures via the least squares *** experiments,the sensor's ability to accurately measure directional forces up to 1.2 N in the Z axis was validated,demonstrating an average relative error of only 1.18%compared with the full-scale *** results indicate that this tactile sensor can provide effective 3-axial force sensing during surgical operations,such as grasping and pulling,and in ex vivo testing with a porcine *** compact size,high precision,and integrability of the sensor establish solid foundations for clinical application in the operating theater.

关键词： axial surgery intensified

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Nitrogen-doping assisted local chemical heterogeneity and mechanical properties in CoCrMoW alloys manufactured via laser powder bed fusion

Advanced Powder Materials

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advanced Powder Materials 2024年第5期3卷 46-57页

作者： Wenting Jiang Ruidi Li Junyang He Song Ni Li Wang Zibin Chen Yi Huang Caiju Li Jianhong Yi Min Song State Key Laboratory of Powder Metallurgy Central South UniversityChangsha 410083China College of Materials and Advanced Manufacturing Hunan University of TechnologyZhuzhou 412007China Research Institute for Advanced Manufacturing Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong KongChina Department of Design and Engineering Faculty of Science and TechnologyBournemouth UniversityPooleDorsetBH125BBUK Materials Research Group Department of Mechanical EngineeringUniversity of SouthamptonSouthamptonSO171BJUK Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming 650093China

CoCrMoW alloys with different nitrogen(N)additions(0,0.05,0.1,and 0.2 wt%)were prepared via laser powder bed fusion(LPBF).The effects of N content on the microstructure and mechanical properties were *** results indicate that the LPBFed CoCrMoW alloy with 0.1 wt%N addition(0.1 N alloy)shows the best combination of mechanical properties with a yield strength of~983 MPa and an elongation of~19%.Both the LPBF process and the N addition impose great effects on suppressing theγtoεmartensitic transformation,resulting in a decrease in the width and amount ofεlaths/stacking ***,the N addition promotes the segregation of elements Mo,W,and Si along the cellular sub-grain boundaries(CBs),forming fine and discontinuous precipitates rich in Mo,W and Si along the CBs in the 0.1 N alloy,but dense and continuous(Mo,W)5Si_(3)precipitates along the CBs in the 0.2 N ***(Mo,W)5Si_(3)precipitates with a tetragonal structure were observed and characterized for the first time in the Co-Cr based *** negative mixing enthalpy between the non-metallic elements N,Si and the metallic elements Mo,W,Cr,and the rapid solidification induced segregation of high melting point elements such as Mo and W along CBs during LPBF process,synergistically contribute to the chemical heterogeneity in the *** pure FCC matrix,the slightly increased segregation of Mo,W,Si elements and fine precipitates along the CBs contribute to the good combination of strength and elongation of the 0.1 N ***,though pure FCC phase was present in the 0.2 N alloy,the dense and continuous(Mo,W)5Si_(3)precipitates along CBs acted as nucleation sites for cracks,deteriorating the elongation of the ***,it is possible to tune the mechanical properties of the LPBFed CoCrMoW alloy by adjusting the local chemical heterogeneity.

关键词： CoCrMoW alloy Laser powder bed fusion Nitrogen addition Mechanical properties Precipitation behaviors

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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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Investigations of corrosion inhibition of ethanolic extract of Dillenia suffruticosa leaves as a green corrosion inhibitor of mild steel in hydrochloric acid medium

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Corrosion Communications 2024年第3期15卷 52-62页

作者： Ukashat Mamudu Jose Hernandez Santos Saviour A.Umoren Mohamad Sahban Alnarabiji Ren Chong Lim Centre for Advanced Material and Energy Sciences(CAMES) Universiti Brunei Darussalam Faculty of Science Universiti Brunei Darussalam Interdisciplinary Research Centre for Advanced Materials King Fahd University of Petroleum and Minerals Department of Microbial and Molecular Systems Center for Sustainable Catalysis and Engineering(CSCE) KU Leuven

The utilization of plant extracts as environmentally friendly corrosion inhibitors has garnered considerable interest in the field of metallic corrosion protection. The primary objective of this study was to explore a new approach for utilizing Dillenia suffruticosa leaves extract(DSLE) to provide corrosion protection for mild steel(MS) in a molar hydrochloric acid solution. The phytocompounds responsible for corrosion prevention were extracted using ethanol with Soxhlet apparatus. The effectiveness of the extract as a corrosion inhibitor was evaluated through gravimetric and electrochemical methods, along with surface analysis of the corroded steel samples. This analysis involved the use of scanning electron microscopy(SEM) and Fourier-transformed infrared spectroscopy(FTIR) to examine the specimens with and without addition of the extract as inhibitor. The findings demonstrate that DSLE exhibits notable efficacy as a corrosion inhibitor for MS in an acidic environment. The inhibition efficiency(IE)demonstrates a positive correlation with the concentration of the extract, ultimately reaching a maximum value of 81.4 % at the optimum concentration of 1000 mg/L of the extract investigated. Potentiodynamic polarization data revealed that the DSLE functions as a mixed-type inhibitor.

关键词： Plant extract Mild steel Acid corrosion Corrosion inhibition Dillenia suffruticosa

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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