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Effect of Brass Content and Speed of Cut on Machining Outcomes of Copper–2% Silver Alloy

Journal of The Institution of Engineers (India): Series D 2024年 1-7页

作者： Prashanth, Mrudula Prasad, N. J. Krishna Kumar, B. S. Ajay Satish, N. Department of Industrial Engineering and Management Bangalore Institute of Technology Bengaluru Visveswaraya Technological University Belgaum India Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Mechanical Engineering Bangalore Institute of Technology Bengaluru Visveswaraya Technological University Belagavi Karnataka India Department of Robotics and Artificial Intelligence Bangalore Institute of Technology Bengaluru Visveswaraya Technological University Belagavi India

Copper is one of the ductile materials which is difficult to machine, but extensively used in variety of applications such as electrical, thermal and nuclear. Turning is one of the predominantly used lathe operation, and the current work focuses on turning different alloys of copper at various compositions of alloying elements to investigate the effect of the machining parameter, i.e. speed of cut on cutting force, thrust force, unit power consumption and machinability which marks the effect as machining outcomes. The work also focuses on the effect of the alloys formed out of varying compositions of brass on the machining outcomes of copper–2wt% silver alloy. The work concludes on, the forces exerted on tool, unit power consumption and machinability to be least at 9% brass addition to copper silver alloy. © The Institution of Engineers (India) 2024.

关键词： Brass

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Abusive Activity Detection with Multi-Modality Based on Convolutional Neural Network

Abusive Activity Detection with Multi-Modality Based on Conv...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jisoo Kim Hyebin Ahn Byounghyun Yoo Center for Artificial Intelligence Korea Institute of Science and Technology Artificial Intelligence and Robotics KIST School Korea University of Science and Technology

Abusive activity frequently occurs in various places, including nursing homes for the elderly. However, it is difficult to detect because it has various forms and is not easy to define. Therefore, in this study, we try to detect using the Convolutional Neural Network (CNN). Data based on multimodality (image and sound) was used to improve detection performance. In case of the image, a Motion History Image (MHI) was used to reflect motion information. The developed multi-modality-based detection system showed a performance of 0.807 (Accuracy) and 0.858 (F1 score). By comparing with the system using only image or sound, there is an improvement of 0.132 ~ 0.17 (Accuracy) and 0.094 ~ 0.138 (F1 score)

关键词： Image recognition Neural networks Speech recognition Medical services Signal processing Convolutional neural networks History

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3D Ball Trajectory Reconstruction of a Ballistic Shot from a Monocular Basketball Video 49

3D Ball Trajectory Reconstruction of a Ballistic Shot from a...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chao, Vanyi Jamsrandorj, Ankhzaya Oo, Yin May Mun, Kyung-Ryoul Kim, Jinwook University of Science and Technology Ai Robotics Daejeon34113 Korea Republic of University of Science and Technology Human Computer Interface and Robotics Daejeon34113 Korea Republic of Center for Artificial Intelligence Korea Institute of Science and Technology Seoul02792 Korea Republic of

ISBN: (纸本)9798350331820

3D ball location in sports can be advantageous and has many applications such as automatic scoring system, ball trajectory highlight and offside detection system for football game. Currently, this task can be solved through the use of costly multi-view approaches or the legacy physics-based methods that require complicated setups and human intervention. In this paper, we propose a method to reconstruct the 3D ball trajectory of the basketball projectile shot by using only a single view. The method relies on accurate camera calibration and 2D ball detection of sequence images. Extra information in the 3D world also plays an important role in improving the 3D reconstruction. The experimental result illustrates the accurately reconstructed 3D trajectory when enough information is given. © 2023 IEEE.

关键词： Ballistics

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A Residual Attention-based EfficientNet Homography Estimation Model for Sports Field Registration 49

A Residual Attention-based EfficientNet Homography Estimatio...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Oo, Yin May Jamsrandorj, Ankhzaya Chao, Vanyi Mun, Kyung-Ryoul Kim, Jinwook Ai Robotics University of Science and Technology Daejeon34113 Korea Republic of Human Computer Interface and Robotics University of Science and Technology Daejeon34113 Korea Republic of Center for Artificial Intelligence Korea Institute of Science and Technology Seoul02792 Korea Republic of

ISBN: (纸本)9798350331820

Accurate sports field registration plays a crucial role in computer vision for effective team sports analysis, as it provides the ability to track and recognize the positions, movements, and interactions of players from image space to real-world field space. In this paper, we propose a two-stage deep learning framework for sports field registration. In the first-stage network, we utilize a Residual EfficientNet-Attention U-Net architecture to estimate the initial homography matrix using predefined keypoints to register sports fields. Subsequently, our second-stage network refines the initial homography for improved accuracy. We evaluate our proposed method on the public sports datasets to show its outperform results against the state-of-the-art methods in some evaluation metrics. © 2023 IEEE.

关键词： Sports

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Attention-enhanced BLSTM Network for Liquid Volume Estimation based on Tactile Sensing

Attention-enhanced BLSTM Network for Liquid Volume Estimatio...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Su, Yuanzhe Wang, Jingnan Huang, Binhua Li, Xiaoyu Liu, Yiwen Liu, Chengliang Zhou, Zhenning Yi, Zhengkun Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Siat Branch Shenzhen518055 China

ISBN: (纸本)9798350327182

The task of liquid volume estimation based on tactile sensing is challenging. Previous researches mainly focused on the classification of different contents in the container, rather than achieving accurate estimation of a certain property of the contents in container, such as volume or viscosity. In addition, few studies are conducted for the regression task with advanced tactile sensors, but the performance is unsatisfactory. We propose a new deep-learning algorithm for processing the tactile signals collected from the photoresistors in the task of liquid content estimation. The proposed algorithm doesn't need hand-crafted features. In addition, we use attention after BLSTM, so that more important features can be selected to achieve better performance for liquid volume estimation. We utilize a bi-directional Long Short-Term Memory (BLSTM) architecture, which allows for the incorporation of information from both preceding and subsequent time, thereby providing a more comprehensive and informative feature set for learning. Additionally, we employ an attention mechanism following the BLSTM, allowing for the selection of more critical features and improved performance in liquid volume estimation. To prove the effectiveness of our method, we conduct experiments utilizing tactile sensors on a physical robot, achieving superior performance in liquid volume estimation. © 2023 IEEE.

关键词： Containers

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Towards Conscious Service Robots

arXiv

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arXiv 2025年

作者： Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics the Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

Deep learning’s success in perception, natural language processing, etc. inspires hopes for advancements in autonomous robotics. However, real-world robotics face challenges like variability, high-dimensional state spaces, non-linear dependencies, and partial observability. A key issue is non-stationarity of robots, environments, and tasks, leading to performance drops with out-of-distribution data. Unlike current machine learning models, humans adapt quickly to changes and new tasks due to a cognitive architecture that enables systematic generalization and meta-cognition. Human brain’s System 1 handles routine tasks unconsciously, while System 2 manages complex tasks consciously, facilitating flexible problem-solving and self-monitoring. For robots to achieve human-like learning and reasoning, they need to integrate causal models, working memory, planning, and metacognitive processing. By incorporating human cognition insights, the next generation of service robots will handle novel situations and monitor themselves to avoid risks and mitigate errors. Copyright © 2025, The Authors. All rights reserved.

关键词： Problem solving

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3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation

3D Face Reconstruction with the Geometric Guidance of Facial...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zidu Wang Xiangyu Zhu Tianshuo Zhang Baiqin Wang Zhen Lei State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

3D Morphable Models (3DMMs) provide promising 3D face reconstructions in various applications. However, existing methods struggle to reconstruct faces with extreme expressions due to deficiencies in supervisory signals, such as sparse or inaccurate landmarks. Segmentation information contains effective geometric contexts for face reconstruction. Certain attempts intuitively depend on differentiable renderers to compare the rendered silhouettes of reconstruction with segmentation, which is prone to issues like local optima and gradient instability. In this paper, we fully utilize the facial part segmentation geometry by introducing Part Re-projection Distance Loss (PRDL). Specifically, PRDL transforms facial part segmentation into 2D points and re-projects the reconstruction onto the image plane. Subsequently, by introducing grid anchors and computing different statistical distances from these anchors to the point sets, PRDL establishes geometry descriptors to optimize the distribution of the point sets for face reconstruction. PRDL exhibits a clear gradient compared to the renderer-based methods and presents state-of-the-art reconstruction performance in extensive quantitative and qualitative experiments. Our project is available at https://***/wang-zidu/3DDFA-V3.

关键词： Geometry Image segmentation Solid modeling Computer vision Three-dimensional displays Annotations Face recognition

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An Information-Theoretic Approach for Heterogeneous Differentiable Causal Discovery

SSRN

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SSRN 2024年

作者： Zhou, Wanqi Bai, Shuanghao He, Yicong Zhao, Qibin Chen, Badong Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China RIKEN AIP Tokyo Japan

With the advancement of deep learning, a variety of differential causal discovery methods have emerged, inevitably attracting more attention for their excellent scalability and interpretability. However, these methods often struggle with complex heterogeneous datasets that exhibit environmental diversity and are characterized by shifts in noise distribution. To this end, we introduce a novel information-theoretic approach designed to enhance the robustness of differential causal discovery methods. Specifically, we integrate Minimum Error Entropy (MEE) as an adaptive error regulator into our structure learning framework. MEE effectively reduces error variability across diverse samples, enabling our model to adapt dynamically to varying levels of complexity and noise. This adjustment significantly improves the precision and stability of the model. Extensive experiments on both synthetic and real-world datasets have demonstrated significant performance enhancements over existing methods, affirming the effectiveness of our approach. We commit to providing our code upon publication for further experimentation and verification. © 2024, The Authors. All rights reserved.

关键词： Information theory

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Optimized Algorithm for Optical Phased Array Beam Steering

Optimized Algorithm for Optical Phased Array Beam Steering

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Optoelectronics Global Conference (OGC)

作者： Jiarui Zhang Binghui Li Wu Shi Caiming Sun Aidong Zhang Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China

ISBN: (数字)9798350391466

ISBN: (纸本)9798350391473

Silicon-based optical phased array (OPA) offer significant potential for high-density integration and flexible beam steering, making them highly promising for applications in miniaturized LiDAR and optical wireless communication. However, research into algorithms for efficient beam steering in OPA remains limited. To our knowledge, this paper is the first to explore the differences in beam deflection using various modulation algorithms under experimental conditions. In our study, we designed a uniform 64-channel OPA chip based on SOI technology, fabricated using a standard silicon photonics platform. By employing an optimized modulation algorithm, we achieved a broad steering range of 60 degrees horizontally at a wavelength of 1550 nm, with an angular resolution of 0.3 degrees.

关键词： Phased arrays Integrated optics Wireless communication Beam steering Laser radar Optical device fabrication Optical modulation Thermal stability Standards Optical arrays

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A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

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Computers, Materials & Continua 2023年第2期74卷 2817-2838页

作者： Noureldin Eissa Uswah Khairuddin Rubiyah Yusof Ahmed Madani Centre for Artificial Intelligence&Robotics Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of Computer Engineering Arab Academy for ScienceTechnology and Maritime TransportCairo2033Egypt Department of Mechanical Precision Engineering Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia

Epigenetics is the study of phenotypic variations that do not alter DNA *** epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human *** of these alterations is DNA methylation;an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in ***,studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA ***,microarray technologies;such as Illumina Infinium BeadChip assays;are used to study DNA methylation at an extremely large number of varying *** each DNA methylation site,a beta value(β)is used to reflect the methylation ***,clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers aswell as identify the relevant loci without user *** study proposed a Nested Big Data Clustering Genetic Algorithm(NBDC-GA);a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation *** efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas(TCGA);a cancer genomics program created by the NationalCancer institute(NCI)and the NationalHuman Genome Research *** performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm(IGA)that was tested using the same data *** NBDC-GA outperformed the IGA in terms of convergence ***,the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximumof 67%and of 94.5%for individual cancer type and collective cancer,*** proposed NBDC-GA was also able to identify two chromosomes with highly contrastingDNAmethylations activities th

关键词： Cancer clustering DNA methylation feature selection metaheuristic technique the cancer genome atlas

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