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Entangled chip removal utilizing mass-spring model with mobile manipulator

CIRP Annals 2025年

作者： Takahashi, Ryuki Kimura, Hayato Kakinuma, Yasuhiro Department of System Design Engineering Faculty of Science & Technology Keio University 3-14-1 Hiyoshi Yokohama Kohoku-ku Japan

This study aims to automate the removal of entangled chips in manufacturing environments using a mobile manipulator. Despite advancements in factory automation technology, this task often necessitates manual intervention. Accordingly, this study proposes a deformable chip model (based on a reconfigurable mass-spring system per frame), incorporating visual chip tracking data and force reactions. Consequently, to evaluate the removal state, an efficient removal strategy is derived by defining the instantaneous stiffness and energy absorption rate. Validation of system performance through two removal tests demonstrates the feasibility of automating entangled chip removal, contributing to enhanced operational efficiency in fully automated production lines. © 2025 The Author(s)

关键词： Factory automation

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Large circuit models: opportunities and challenges

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science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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Assessment of Heart-Attack Prediction using Fuzzy Rule Based system 8

Assessment of Heart-Attack Prediction using Fuzzy Rule Based...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Saratkar, Saniya Chaudhari, Aarti Thute, Trupti Raut, Rohini Thakre, Gayatri Kumar, Hemant Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442107 India

ISBN: (纸本)9798350391770

This paper evaluates the prediction of heart attacks with a fuzzy rule-based system (FRBS). Since heart disease is the world's leading cause of death, precise prediction models are crucial for prevention and early intervention. With its reputation for managing imprecise and ambiguous data, fuzzy logic presents a viable method for raising prediction accuracy in medical diagnostics. In order to capture the complexity and unpredictability of cardiovascular risk variables, fuzzy rules are used in this work to investigate the creation and assessment of a fuzzy rule-based system (FRBS) for heart attack prediction. The efficacy and dependability of the FRBS in identifying those at high risk of heart attack are shown via testing and comparison with conventional prediction models. The results highlight fuzzy logic's potential to improve cardiovascular predictive analytics. © 2024 IEEE.

关键词： Prediction models

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Deep Learning to Classify Bacterial Species in the same Genus

Deep Learning to Classify Bacterial Species in the same Genu...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Sheela, Sherin Piang, May Phu Sakorntanant, Sakda Poomrittigul, Suvit Treebupachatsakul, Treesukon School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Biomedical Engineering Thailand Pathumwan Institute of Technology Faculty of Science and Technology Department of Software Engineering and Information System Bangkok Thailand School of Information Technology King Mongkut's Institute of Technology Ladkrabang Thailand

ISBN: (纸本)9798350371888

Bacterial strains in the same genus share highly similar morphology, gram-staining characteristics, colony sizes, and spatial arrangements. Therefore, identifying them by deep learning can be quite challenging. This study aimed to assess the classification of 7 species of bacteria from 2 genera of Bacillus and Vibrio by using 8 Convolutional Neural Network (CNN) models. We implemented Python programming along with Keras API within the Jupyter Notebook. The models were constructed and evaluated under unbalanced and balanced datasets by augmentation (rotation, flip, etc.). Transfer learning with fine-tuning, and pre-processing of mixup and label smoothing were also applied to reduce overfitting and enhance generalization. Based on the experimental results on private dataset, the results of InceptionResNetV2 emerged as the top-performing model with a notable accuracy of 82.8%, 88.6% precision, 78.4% recall, and 78.0% F1-score when label smoothing was applied at 0.5 on balanced dataset. © 2024 IEEE.

关键词： Neural network models

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Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

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Multimedia Tools and Applications 2025年第16期84卷 15735-15761页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

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Lithium-Ion Battery State-of-Charge and State-of-Health Co-Estimation with Feasible Real-Time Implementation 8

Lithium-Ion Battery State-of-Charge and State-of-Health Co-E...

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8th CAA International Conference on Vehicular Control and Intelligence, CVCI 2024

作者： Shao, Mingding Chen, Jian Xu, Ke Wang, Yong School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Mechanical Engineering Zhejiang University Hangzhou China Wanxiang Al23 Systems Corp. Department of System Engineering Hangzhou China

ISBN: (纸本)9798331504892

The key to improving the efficiency of lithium-ion battery systems and reducing safety incidents is that the battery management system can accurately estimate the state of lithiumion batteries, including the state of charge and the state of health. At present, the commonly used algorithms do not achieve an effective balance between accuracy and complexity, and it is difficult to apply them in practice. Therefore, on the basis of the existing algorithms, this paper proposes to reduce the order of the equivalent circuit model, simplify the observer polynomial, achieve low computational complexity, and use a processor with limited storage to complete the estimations. The simulation and experimental results confirm that the maximum absolute error of the state of charge and state of health of the lithium-ion battery systems fully meet the requirements of practical use. Compared with the reference method, there is no obvious difference in convergence speed and stability, but the run time is relatively faster. The proposed method has a significant simplification effect. © 2024 IEEE.

关键词： State of charge

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Analyzing Code Injection Attacks on Applications of Android Devices and Emulator 9

Analyzing Code Injection Attacks on Applications of Android ...

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9th IEEE International Conference on Computing, engineering and design, ICCED 2023

作者： Erfina, Adhitia Hidayat, Rahmat Rizaldi, Rafli Radya Puput Maulana, Riko Sembiring, Falentino Nusa Putra University Faculty of Engineering Computing and Design Departement of Infomation System Sukabumi Indonesia Budi Luhur University Master of Computer Science Faculty of Information Technology Jakarta Indonesia

ISBN: (纸本)9798350370126

The Android operating system released by Google in 2007 has experienced very significant development. In 2019, the number of Android application users reached 2.5 billion. One of the contributing factors is the nature of the android application, which is very flexible and easy to install. Users can download APK files on the internet. Generally, APK files are stored in archive format, so they are pretty easy to manage. However, APK files may be used to spread malware behind this convenience. This study aims to provide an overview of the process of planting malware/backdoor on Android applications and how to prevent it. This study initiated the injection of malicious code on five android applications, namely WhatsApp, Telegram, DroidCam, Fifa Mobile, and Tokopedia. According to the results, four applications were successfully injected, while one application failed. The failure most likely occurred as a result of the application's source code being obfuscated. Obfuscation is therefore strongly advised for Android applications to prevent code injection that could potentially lead to malware insertion. © 2023 IEEE.

关键词： Android (operating system)

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Data Processing and Pattern Recognition for Electronic Nose 3

Data Processing and Pattern Recognition for Electronic Nose

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Raymond, David Chaudhri, Shivnath Shaikh, Kahkasha Datta Meghe Institute of Higher Education and Research Faculty of Enfineering and Technology Department of Computer Science and Medical Engineering Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Enfineering and Technology Depertment of Computer Science and Design Wardha India

ISBN: (纸本)9798331509675

Electronic nose (e-nose) technology has become a powerful tool for identifying and evaluating complex scents in a variety of contexts, such as environmental monitoring, medical diagnostics, and food quality control. This review study looks at how important pattern recognition and data processing are to improving e-nose system performance. This study covered challenges, including data complexity, cross-sensitivity and interference, sensor drift, and a lack of standardization. The article covers several future directions, such as the use of artificial intelligence (AI) and machine learning to advance pattern recognition and predictive analytics, integration with the Internet of Things (IoT) and big data for real-time monitoring, and advancements in sensor technology for improved sensitivity and *** research work is underway towards the development of low power flexible sensors, improved multi sensor fusion Methodologies, smart adaptive algorithms, Application for personalized health monitoring and diagnostics as well to make it green technologies. To move forward with emerging technologies and for e-nose technology to be applicable, it will have to overcome these weaknesses. © 2025 IEEE.

关键词： Electronic nose

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The influence of culture on creativity in ideation: a review

The influence of culture on creativity in ideation: a review

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2024 International design Society Conference, design 2024

作者： Gong, Zhengya Gonçalves, Milene Nanjappan, Vijayakumar Georgiev, Georgi V. Center for Ubiquitous Computing University of Oulu Finland Department of Design Organisation and Strategy Faculty of Industrial Design Engineering Delft University of Technology Netherlands School of Computer Science and Information Technology University College Cork Ireland

The relationship between culture and creativity has sparked the interest of researchers for decades. Although researchers have attempted to establish a connection between culture and creativity, the precise relationship between the two remains ambiguous. The current paper examined extant literature on the subject matter and synthesized the relations between culture and creativity in ideation over the past twenty years. The present study expounds upon the utilized samples, measures implemented to assess creativity and culture, and the study results. © 2024 Proceedings of the design Society. All rights reserved.

关键词： Behavioral research

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An action plan for the agri-food sector at the time of the climate and biodiversity crises

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National science Review 2023年第7期10卷 16-18页

作者： Francesco Cherubini Ting Hua Wenwu Zhao Industrial Ecology Programme Department of Energy and Process Engineering Norwegian University of Science and Technology (NTNU) State Key Laboratory of Earth Surface Processes and Resource Ecology Faculty of Geographical Science Beijing Normal University Institute of Land Surface System and Sustainable Development Faculty of Geographical Science Beijing Normal University

Land areas on our planet are facing unprecedented levels of *** than70%of the global ice-free land has been affected by human activities,30%of the land is threatened by degradation,biodiversity and other ecosystem services(ESs) are declining,and climate change is altering ecosystem functioning [1].Currently,agriculture is the dominant form of land use,with grazing land comprising27%and cropland 12%(and more than half of the cropland is used to produce animal feed).Agriculture is responsible for about one-third of global greenhouse gas(GHG) emissions [2] and it is the primary driver of deforestation,habitat destruction,global freshwater withdrawals,and global ocean and freshwater pollution [3].As land is a limited resource,

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