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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

共 48226 条记录，以下是1611-1620 订阅

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Multiresolution Bidirectional Long Short Term Memory Network-Based Motor Bearing Fault Diagnosis Method 6

Multiresolution Bidirectional Long Short Term Memory Network...

引用

6th international conference on robotics, Control and Automation engineering, RCAE 2023

作者： Li, Ling Li, Jiaxin Wang, Menglong Xiang, Junlin School of Electric and Engineering Changsha University of Science and Technology Changsha China

ISBN: (纸本)9798350315301

In the realm of long-term production and operation processes, the motor system is often susceptible to performance degradation, consequently leading to frequent operational failures that substantially threaten overall system safety. The conventional approach to motor bearing fault diagnosis, predicated on deep learning techniques, grapples with limitations arising from noise interference during the acquisition of bearing vibration signals and incomplete analysis of said signals, thus leading to restricted diagnostic precision. In response to these limitations, this paper introduces a novel motor-bearing fault diagnosis methodology grounded in Multiresolution Bi-Directional Long and Short-Term Memory Networks (MR-BILSTM). Initially, we construct a multiple optimization variational modal decomposition model tailored to enhance the filtering of motor-bearing vibration signals. Subsequently, a weighted kurtosis factor is introduced to meticulously identify the decomposition-filtered vibration signals characterized by diverse resolutions that exhibit the highest sensitivity to fault signals. Following this selection process, a bi-directional long and short-term memory network model is enlisted to comprehensively scrutinize the critical vibrational signal features extracted from various resolutions, ultimately culminating in diagnosing motor bearing faults. Ultimately, the proposed method's efficacy is rigorously validated using the Case Western Reserve bearing fault diagnosis dataset. © 2023 IEEE.

关键词： Failure analysis

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Utilizing language models for Chinese automated disease diagnosis 3

Utilizing language models for Chinese automated disease diag...

引用

3rd international conference on Electronics technology and Artificial Intelligence, ETAI 2024

作者： Yu, Haowen Li, Mingcheng Zhang, Lihua Academy for Engineering and Technology Fudan University Shanghai China Shanghai China Jilin Provincial Laboratory of Intelligence Science and Engineering Changchun China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China

ISBN: (纸本)9781510683143

Symptom-based automated disease diagnosis involves an iterative process of gathering potential symptoms through natural language interactions with patients. Previous research has made use of Reinforcement Learning (RL) techniques to optimize policy networks for symptom inquiry and disease diagnosis, yielding promising results. However, reinforcement learning methods encounter various challenges such as the risk of getting stuck in suboptimal solutions, limited efficacy in training, and difficulties in designing appropriate reward functions, especially when confronting large decision spaces. To address these challenges, this paper converts policy learning in automated disease diagnosis into generative task-QA by utilizing language models in a fully supervised way. We further introduced beam search decoding and an early stop mechanism to facilitate efficient interactive symptom queries and improve the efficiency of the diagnostic process, respectively. To assess the efficacy of our approach, we conducted extensive experiments on three Chinese real-world medical dialogue datasets: Dxy, MUZHI, and IMCS. Preliminary experimental results demonstrate that our method is comparable to or better than previous RL methods in terms of symptom recall and diagnostic accuracy. © 2024 SPIE.

关键词： Self-supervised learning

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IoT-Based AI-Integrated Device for Farmers: A Recommendation System for Crop Cultivation 6

IoT-Based AI-Integrated Device for Farmers: A Recommendation...

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6th international conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Khan, Rafatuzzaman Ahmed, Abrar Mahi, Minhazul Islam Ahmed, Jubair Hoque, Navidul Akhtaruzzaman, M. Islam, Md Shofiqul Sattar, Abdus Mokhlesur Rahman, Maj Md Krishna, T Gopi Jahan, Hosney Baki, Ramisha Fariha Department of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798350385779

Agriculture serves as the primary source of livelihood for the majority of the population. However, crop yields in the country often remain low due to various factors, including prolonged cultivation of the same crops, insufficient knowledge regarding fertilizer types and quantities, and susceptibility to crop diseases in Bangladesh. Previously, several studies were conducted, but the equipment used in the research was too cost-ineffective. To address these challenges, a comprehensive system leveraging advanced technology and data analytics methodologies was developed to assist Bangladeshi farmers. This system utilizes sensors to collect data on moisture levels and soil quality, which are then analyzed to recommend suitable crops and necessary fertilizers for specific fields. Additionally, the system includes a module for detecting potato leaf diseases, aiding farmers in identifying and treating this common ailment. Moreover, the system is capable of calculating the required amount of fertilizer for the field. A thorough evaluation involving farmers was conducted to assess the system's effectiveness, revealing that it performs efficiently in providing recommendations for crop selection, fertilizer application, and plant disease detection. © 2024 IEEE.

关键词： Precision agriculture

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Utilizing Explainable AI Methodologies: LIME and SHAP, for the classification of Natural Disasters through Machine Learning Algorithms 6

Utilizing Explainable AI Methodologies: LIME and SHAP, for t...

引用

6th Parul international conference on engineering and technology, PICET 2024

作者： Akshitha, K. Roopashree, R. Kodipalli, Ashwini Rao, Trupthi Dept of AI and DS Global Academy of Technology Karnataka Banglore India

ISBN: (纸本)9798350369748

This work investigates the use of machine learning for the classification of natural disasters, with a focus on the Random Forest and XG Boost algorithms with optimized hyperparameters. Explainable AI (XAI) techniques Lime and Shap provide insight into model selections, highlighting crucial characteristics such as 'No Injured' and 'Total Damages' that contribute to XG Boost's 72% accuracy. The impact of these features is graphically represented by the contribution graph. XG Boost outperforms other algorithms, demonstrating its effectiveness in classifying disasters. The study emphasizes how important XAI is for improving accuracy and offering perceptive judgments. In addition to improving model interpretability, it advances the subject of disaster classification and opens the door for other XAI applications. © 2024 IEEE.

关键词： Decision trees

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Hybrid Image Encryption for WISN in Privacy Protection and Security of Public Big Data Using Chaotic Maps

Hybrid Image Encryption for WISN in Privacy Protection and S...

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2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Geddada, Nikhitha Sahu, Aditi Suseela, G. Jayshnav, S. Arora, Suchi Srm Institute Of Science And Technology Department Of Networking And Communications Chennai India Srm Institute Of Science And Technology Department Of Computing Technologies Chennai India

ISBN: (纸本)9798350371567

Image big data has become pervasive in today's world. Every day huge volumes of data are being transferred across systems. Significant advancements have been made in networking and file transfer technologies. Due to the remarkable expansion of network infrastructures, multimedia transmission has become inevitable. Image data is being produced at a faster rate and the variety of data types is expanding rapidly. A number of big data-related surveillance applications use wireless image sensor networks (WISN). Important and sensitive data must be hidden from different hackers trying to break into the system. So, encryption is used to hide sensitive information from various hackers. Lorenz attractor along with the chaotic maps are used to withstand statistical attacks. Dual confusion and dual diffusion are implemented so as to ensure maximum chaos and makes the encryption system strong against attacks. The pixels of the image are scrambled using logistic maps so that only authorized users can decipher it. Through the integration of tent map and logistic map, this study approaches an efficient image encryption algorithm. © 2024 IEEE.

关键词： Sensitive data

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The Fusion of robotics and Artificial Intelligence in Business Management 10

The Fusion of Robotics and Artificial Intelligence in Busine...

引用

10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Geetha, B.T. Kamatchi, A. Thirupathi, J. Dhaliwal, Navdeep Devi, Anusuya V. Shalini, S. Saveetha School of Engineering Simats Department of ECE Tamil Nadu India Srm Institute of Science and Technology Department of Corporate Secretaryship and Accounting and Finanace Tamilnadu Chennai India Institute of Aeronautical Engineering Telangana Hyderabad India Lovely Professional University Phagwara India New Horizon College of Engineering Department of Chemistry Bangalore India J.N.N Institute of Engineering Department of Mathematics India

ISBN: (纸本)9798350382471

In today's fast-paced corporate environment, the integration of robotics and artificial intelligence (AI) in business management offers a revolutionary catalyst. This empirical study examines the complex interrelationship between artificial intelligence and machine learning and the way it might change how companies run. robotics, which has historically been linked to physical automation, combines with AI's cognitive powers to create machines that are capable of autonomously perceiving, thinking, learning, as well as performing tasks. Benefits of the fusion include cost savings, better decision-making, increased customer service, and safety improvements. However, it also presents difficulties, which include cybersecurity concerns, ethical dilemmas, and obstacles to technology integration. Redefining decision-making from reactive to proactive, the integration of robotics and AI enables businesses to predict trends and quickly adjust. A lot has changed in the manufacturing, transportation, health care, and finance sectors, but there are countless applications for this fusion, which includes retail and agriculture. Businesses must acknowledge this fusion of technology and ethics as a necessary component of the ever-evolving business landscape in order to stay competitive. © 2023 IEEE.

关键词： Artificial Intelligence Automation Business Management Integration robotics

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Detecting Plant Disease Using Machine Learning

Detecting Plant Disease Using Machine Learning

引用

2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Patidar, Moksha Mishra, Saurabh Mishra, Trapti Tripathi, Sanuj Kukade, Jyoti Barpha, Vidhya Medi-caps University Information Technology Indore India

ISBN: (纸本)9798350388800

Plant diseases are becoming more common, which is a serious danger to the world's food security. Manual examination is a common component of traditional illness detection procedures, which can cause delays in diagnosis and treatment. Convolutional Neural Networks (CNNs), one type of deep learning technology, have demonstrated encouraging results in automating the detection and categorization of plant diseases in recent years. This work uses high-resolution plant leaf photos to investigate the use of CNNs for plant disease detection. The essential components of the suggested system are picture capture, preprocessing, feature extraction, CNN model training, and illness classification. Model assessment and training are conducted using an extensive dataset that includes tagged photos of both healthy and ill plants. The study assess how well the CNN-based plant disease detection system performs. The efficacy of the CNN-based plant disease detection system is assessed in the study by means of measures including F1-score, recall, accuracy, and precision. The model is also contrasted with conventional techniques to demonstrate its superiority in terms of accuracy and speed. The outcomes show how well the suggested CNN method works to identify different plant illnesses, opening the door for the creation of a trustworthy and effective automated plant disease detection system. © 2024 IEEE.

关键词： Convolutional neural networks

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Motion Analysis of Simple Walking Models Driven by Minimalistic Periodic Input 8

Motion Analysis of Simple Walking Models Driven by Minimalis...

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8th IEEE international conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Yan, Cong Asano, Fumihiko Ritsumeikan University Department of Mechanical Engineering 1-1-1 Nojihigashi Kusatsu Shiga525-8577 Japan Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa923-1292 Japan

ISBN: (纸本)9798350300178

In dynamic bipedal walking, it is generally expected that simple and efficient control methods can generate stable limit cycle gaits. Therefore, we assume that well-designed periodic inputs can achieve excellent walking performance while maintaining stability without relying on feedback. This study compares two typical bipedal walker models, namely the rimless wheel(RW) and the biped, to validate this assumption. The results demonstrate that both models can successfully generate a stable limit cycle gait. By fine-tuning the control parameters, we confirmed that the bipedal model with swing legs exhibited a higher susceptibility to entrainment compared to the RW model. Our work demonstrates the existence of time-symmetric input reaching spatially asymmetric results in walking robots, which has important implications for understanding symmetry breaking in nature and the behavior of complex systems. © 2023 IEEE.

关键词： Motion analysis

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Exploring Homophobic Discourse in Tamil Transcripts: A Comparative Analysis of Sentiment Classification Models using TF-IDF Vectorization 2

Exploring Homophobic Discourse in Tamil Transcripts: A Compa...

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2nd IEEE international conference on advances in Information technology, ICAIT 2024

作者： Sivakumar, Soubraylu Prakash, Jyothi Saravanan, P. Kumar, T. Rajesh Shanmugam, Selvanayaki Kolandapalayam Rajalakshmi, Ratnavel SRM Institute of Science and Technology SRM Nagar Tamilnadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Ashland University OH United States School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350383867

This research delves into the linguistic patterns of homophobic discourse within Tamil transcripts, employing sentiment analysis methods and TF-IDF vectorization for feature representation. Homophobia, characterized by prejudice, discrimination, or antagonism directed against individuals based on their sexual orientation, is a pervasive issue addressed in this study. Various ML algorithms, such as Support Vector Machine Classifier (SVM), Naive Bayes Classifier (NBC), Logistic Regression, and Gradient Boosting (GB), are utilized to discern and assess their effectiveness in distinguishing between homophobic and non-homophobic language. The dataset has been derived from various online platforms, prominently including YouTube, alongside other sources. Performance metrics including accuracy, F1 score, precision, and support are employed to gauge the models' performance. The outcomes shed light on the prevalent discriminatory language within the Tamil-speaking community and offer insights into combating homophobia through computational linguistic techniques. Notably, SVM demonstrated the highest accuracy of 88.18% in the classification task. ©2024 IEEE.

关键词： Logistic regression

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A Handheld Hydraulic Cardiac Catheter with Omnidirectional Manipulator and Touch Sensing

A Handheld Hydraulic Cardiac Catheter with Omnidirectional M...

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IEEE international conference on robotics and Automation (ICRA)

作者： Chi Cong Nguyen Davies, James Mai Thanh Thai Trung Thien Hoang Phuoc Thien Phan Zhu, Kefan Dang Bao Nhi Tran Van Anh Ho Hung Manh La Hoang-Phuong Phan Lovell, Nigel H. Thanh Nho Do UNSW Sydney Fac Engn Grad Sch Biomed Engn Kensington Campus Sydney NSW 2052 Australia RMIT Univ Sch Sci Engn & Technol Vietnam Campus Ho Chi Minh City Vietnam Japan Adv Inst Sci & Technol JAIST Sch Mat Sci Nomi Ishikawa Japan PRESTO Japan Sci & Technol Agcy Kawaguchi Saitama Japan Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA UNSW Sydney Fac Engn Sch Mech & Mfg Engn Kensington Campus Sydney NSW 2052 Australia

ISBN: (纸本)9798350323658

Atrial fibrillation (AF) is mostly treated via robotic catheter-based cardiac ablation procedures. Over the last few decades, cables or tendon mechanisms are at the core of available cardiac catheters. Despite advances, the use of cables often results in considerable force loss, nonlinear hysteresis, and control challenges. Most catheters are not equipped with force sensing, which increases the risk of the ablation process and decreases their efficacy in clinical settings. In addition, current catheters have a poor user interface and therefore the ablation process requires skilled or trained surgeons to steer the complex motion of the catheter tip within the heart chambers. To improve the cardiac ablation procedure, a new robotic catheter that has the ability to extend its working space without moving its flexible body and a real-time force sensor for safe operation is highly desired. In this work, a new handheld and soft robotic catheter for AF ablation is introduced. The new device consists of several improved components such as a soft manipulator for navigation and bending motion, an ergonomic handheld controller, and a soft force sensor for monitoring tool-tissue contact. The design, modeling, and fabrication of the device are presented and followed by experimental characterizations and ex-vivo validation.

关键词： Soft robotics surgical catheter soft robotic catheter cardiac ablation minimally invasive surgery

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