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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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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on Computer Aided Systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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A Neurobehavioral Evaluation of the Efficacy of 1mA Longitudinal, Anodal TDCS on Multitasking and Transfer Performance

A Neurobehavioral Evaluation of the Efficacy of 1mA Longitud...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Rao, Akash K Uttrani, Shashank Shah, Darshil Menon, Vishnu K Bhavsar, Arnav Roy Chowdhury, Shubhajit Negi, Ramsingh Dutt, Varun Manipal Academy of Higher Education India Indian Institute of Technology Applied Cognitive Science Laboratory Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi India Cognitive Control and Machine Learning Group The Institute of Nuclear Medicine and Allied Sciences Defence Research and Development Organization India

ISBN: (纸本)9781665410205

Multitasking requires rapid switching of attention and cognitive resources between different tasks in a dynamic environment, relying on cognitive processes, such as working memory, executive control, and selective attention. Although studies have investigated the efficacy of various neurobehavioral interventions in improving multitasking capabilities, the effects of longitudinal anodal transcranial direct current stimulation (tDCS) in enhancing multitasking performance have not been investigated. This research investigated the efficacy of 1mA anodal tDCS administered longitudinally on multitasking performance. 42 participants were randomly and equally divided into the experimental and placebo control conditions in this study conducted for 10 days. All participants executed two multitasking tasks on day 1 and received 1mA anodal/placebo tDCS during task training from day 2 to day 8. Various behavioral and neurophysiological measures have been measured. The findings revealed that tDCS had the propensity to augment multitasking capabilities in the trained task but had limited transfer capabilities. EEG-based brain connectivity analysis also revealed the formation of network hubs in the prefrontal and frontal regions, indicating enhanced cortical activation in the beta band. We intend to use these findings to design interventional frameworks to enhance multitasking performance using tDCS. © 2024 IEEE.

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208. Halifax Class – Defect Assessment

208. Halifax Class – Defect Assessment

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作者： Authors: MacDonald, G. Defence Research and Development Canada Valcartier Research Centre Quebec QC (CAN) Lloyd's Register Applied Technology Group Halifax NS (CAN)

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Adaptive optimization of natural coagulants using hybrid machine learning approach for sustainable water treatment

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Scientific Reports 2025年第1期15卷 1-20页

作者： Randive, Pallavi Bhagat, Madhuri S. Bhorkar, Mangesh P. Bhagat, Rajesh M. Vinchurkar, Shilpa M. Shelare, Sagar Sharma, Shubham Beemkumar, N. Hemalatha, S. Kumar, Parveen Kedia, Ankit El Sayed Massoud, Ehab Gupta, Deepak Lozanovic, Jasmina Civil Engineering Department KDK College of Engineering Maharashtra Nagpur 440024 India Civil Engineering Department Yeshwantrao Chavan College of Engineering (YCCE) Nagpur Maharashtra Nagpur 441110 India Department of Civil Engineering G H Raisoni College of Engineering Maharashtra Nagpur 440016 India Mechanical Engineering Department G H Raisoni College of Engineering Maharashtra Nagpur 440016 India Department of Mechanical Engineering Priyadarshini College of Engineering Maharashtra Nagpur 440019 India University Centre for Research and Development Chandigarh University Punjab Mohali 140413 India Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Technical Sciences Western Caspian University Baku Azerbaijan Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to Be University) Karnataka Bangalore India Department of Mechanical Engineering Raghu Engineering College Andhra Pradesh Vishakhapatnam 531162 India Department of Mechanical Engineering Chandigarh College of Engineering Chandigarh Group of Colleges Punjab Jhanjeri Mohali 140307 India Department of Mechanical & Aerospace Engineering NIMS Institute of Engineering & Technology NIMS University Rajasthan Jaipur India College of Applied Sciences Dhahran Al-Janoub King Khalid University Abha Saudi Arabia Department of Mechanical Engineering Graphic Era Hill University Uttarakhand Dehradun 248002 India Department of Mechanical Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun 248002 India Department of Engineering FH Campus Wien - University of Applied Sciences Clinical Engineering Favoritenstraße 226 B.3.01 Vienna 1100 Austria

The efficiency optimization methods for natural coagulants are often restricted due to non-scientific trial-and-error approaches. They are inaccurate in predicting the complex interactions of jet mixing parameters, coagulant dosage, and environmental conditions. To overcome these obstacles, this research paper proposes advanced hybrid models in machine learning to enhance flocculation efficiency. We use the CatBoost model with the NTK to learn the intricate nonlinear interactions among jet velocity, mixing time, coagulant dose, pH, and turbidity. CatBoost is effective for dealing with categorical data like diverse coagulants. Meanwhile, NTK boosts the model’s generalization capability, especially when the sample size becomes small or experimental datasets are applied. Lastly, SOMs and MARS are used to identify pattern recognitions in tracing the crucial interaction among mixing parameters. Reinforcement learning techniques—that include DDPG and SAC for dynamic optimization of jet velocity, mixing time, and coagulant dosage—optimize the model in real time. Utilizing NAS and Hyperband to automate model tuning, the timestamp was reduced by 40%. The proposed models heavily improve the efficiency of the flocculation process by 20–25% and allow for a good predictive accuracy of 95–97%. Paramount, however, is that the model has interpretability properties assured by SHAP and counterfactual explanations, which would give actionable insights into the most influencing factors on the efficiency of flocculation. This work represents a substantial advancement for the discipline since it introduces robust, interpretable, and real-time optimization methods to offer a practical tool through which improvement of water treatment processes would be made both sustainable and efficient. © The Author(s) 2025.

关键词： CatBoost Flocculation Jet mixing Machine learning Reinforcement learning Scenarios

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Inertial Measurement Unit to Segment Calibration Based on Physically Constrained Pose Generation

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SICE Journal of Control, Measurement, and System Integration 2020年第3期13卷 122-130页

作者： Maruyama, Tsubasa Toda, Haruki Ishii, Wataru Tada, Mitsunori Japan Artificial Intelligence Research Center AIST Japan Robotics Applied Research Group Advanced Technology Research Division Research & Development Section Advanced Technology Center Yamaha Motor Co. Ltd. Fukuroi Engineering Center Japan

The accuracy of a motion capture (MoCap) system based on inertial measurement units (IMUs) depends on the IMU-to-segment (I2S) calibration, in which the IMU alignment relative to a body segment is calculated using a reference pose such as a standing T-pose. This study proposes a novel I2S calibration system for a reliable MoCap system. In this system, the reference pose for calibration is constrained by physical objects, and this pose is generated by an optimization-based method which incorporates the IMU measurements with the physical constraints between the body and the object. To demonstrate the system, we estimated the chair-sitting and half-squat motions based on the calibration using a chair and estimated the center-of-mass movements of a rider when riding a motorcycle based on the calibration using a motorcycle. The experiments confirmed the improvements of the motion estimation accuracy by the proposed system, in both chair-sitting and half-squat motion and center-of-mass tracking of a rider. Furthermore, the proposed system enables the I2S calibration using product-use poses. © Taylor & Francis group, LLC 2020.

关键词： Calibration

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Artificial intelligence-based health management system: Unequally spaced medical data analysis

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NTT Technical Review 2018年第8期16卷 24-28页

作者： Kurasawa, Hisashi Fujino, Akinori Hayashi, Katsuyoshi Networked Robot and Gadget Project NTT Service Evolution Laboratories Japan Learning and Intelligent Systems Research Group TT Service Evolution Laboratories Japan Social Device Technology Laboratory Device Technology Laboratories and Research and Development Planning Department NTT Japan

We are developing an artificial intelligence (AI)-based health management system that suggests efficient and effective interventions for keeping people healthy based on the potential risk of disease predicted by using AI. We proposed a new feature extraction model for unequally spaced medical data that improves the disease risk prediction. We introduce the model in detail in this article and also describe an application of the model that demonstrates the improved prediction accuracy. © 2018 Nippon Telegraph and Telephone Corp. All rights reserved.

关键词： Artificial intelligence

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Tubewell platform color as a screening tool for arsenic in shallow drinking water wells in Bangladesh 6th

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6th International Congress on Arsenic in the Environment, AS 2016

作者： Annaduzzaman, M. Biswas, A. Hossain, M. Bhattacharya, P. Alauddin, M. Cekovic, R. Alauddin, S. Shaha, S. KTH-International Groundwater Arsenic Research Group Department of Sustainable Development Environmental Science and Engineering KTH Royal Institute of Technology Stockholm Sweden International Center for Applied Climate Science University of Southern Queensland ToowoombaQLD Australia Department of Chemistry Wagner College Staten IslandNY United States Exonics Technology Center Dhaka Bangladesh

ISBN: (纸本)9781138029415

The development of a simple and low cost technique for determination of arsenic (As) in drinking water wells is an urgent need to accelerate As mitigation policy. The aim of this study was to evaluate the potentiality of tubewell platform color as low-cost, quick and convenient screening tool for As. The result shows strong correlation between the development of red color stain on tubewells platform and As enrichment in the corresponding tubewells water compared to WHO (10 μg/L) and BDWS (50 μg/L), with 99% certainty. The red color stain in the platform indicates 98% sensitivity with WHO (10 μg/L) and BDWS (50 μg/L). With regard to WHO and BDWS, the corresponding efficiency of the platform color as screening tool for As are 97.3% and 97%. This study suggests that platform color can be potentially used for screening tubewells, help users switch to tube wells with low As and facilitate sustainable As mitigation efforts in developing countries. © 2016 Taylor & Francis group, London.

关键词： Arsenic

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Corrigendum to “ Transient multi-objective optimization of solar and fuel cell power generation systems with hydrogen storage for peak-shaving applications ” [Int J Hydrogen Energy (64), 25 April 2024, Pages 220–235]

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International Journal of Hydrogen Energy 2025年 103卷 597-597页

作者： Tao Hai Amit Kumar Saman Aminian Ibrahim Mahariq Mika Sillanpää Hassan Fouad Walid El-Shafai Artificial Intelligence Research Center (AIRC) Ajman University P.O.Box: 346 Ajman UAE Department of Mechanical Engineering GLA University Mathura UP Pin-281406 India Department of Civil Engineering College of Engineering Cihan University-Erbil Erbil Iraq Electrical and Computer Engineering Department Gulf University for Science and Technology Mishref Kuwait Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Functional Materials Group Gulf University for Science and Technology Mubarak Al-Abdullah 32093 Kuwait Kuwait School of Technology Woxsen University Hyderabad Telangana India Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura-140401 Punjab India Division of Research & Development Lovely Professional University Phagwara-144411 Punjab India Department of Civil Engineering University Centre for Research & Development Chandigarh University Gharuan Mohali Punjab India Applied Medical Science Department Community College King Saud University P.O Box 11433 Riyadh Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt

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New foam stabilizing additive for aluminium

New foam stabilizing additive for aluminium

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5th International Conference on Porous Metals and Metallic Foams, MetFoam 2007

作者： Babcsán, N. Kumar, G. S. Vinod Murty, B.S. Moreno, F. García Banhart, J. Materials Development Group BayLOGI Bay Zoltán Foundation for Applied Research Miskolc-Tapolca 3519 Iglói u. 2. Hungary Non-Ferrous Materials Technology Development Centre Hyderabad 500058 India Dept. of Metallurgical and Materials Engineering Indian Institute of Technology Madras Chennai 600036 India Hahn-Meitner-Institut Berlin GmbH Glienicker Strabe 100 14109 Berlin Germany

ISBN: (纸本)9781932078282

Liquid metals are mostly made foamable by Ca additions followed by a thickening period. There is a need for an additive that can be easily admixed to an aluminium alloy melt and makes this melt foamable. We have selected aluminium-based grain refiner composites to test their foamability. TiB 2, TiC or TiAl3 particles were produced in the melt by flux-assisted melting using fluoride salts. The particle size was kept below 1 μm for TiB2 and TiC and around 10 μm for TiAl3. The composites were heated to above their melting point (700C) and were then foamed by either the addition of TiH2 or by injecting gases into the melt directly. Foams were successfully produced using TiB2 and TiC particles, while TiAl3 did not lead to any foam. Foam stability increased from TiB2 to TiC. Ex-situ characterisation of the foams by SEM showed that the particles segregate to the surfaces of the cell walls and lead to almost dense coverages there. Even after dilution of the initial composite a significant amount of foam can be still produced, indicating that these composites are suitable foam stabilizing additive for aluminium alloys and that foams based on small volume fractions of non-metallic additives can be produced.

关键词： Binary alloys

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