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检索条件"任意字段=Scientific Conference on Mechatronics Engineering and Computer Science"

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A Study on Employing Various Tools for Teaching Power Electronics Undergraduate Students 49

A Study on Employing Various Tools for Teaching Power Electr...

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

作者： Singh, Yadvendra Singh, Shakti Bhatnagar, Pallavee Malik, Meena Dewangan, Niraj K Gupta, Krishna Kumar Thapar Institute of Engineering & Technology Electrical & Instrumentation Engineering Department Patiala India Manipal Instiute of Technology Department of Mechatronics Manipal India Chandigarh University Department of Computer Science Mohali India

ISBN: (纸本)9798350331820

Power electronics is an emerging and rapidly advancing technology within the realm of electrical engineering, finding wide-ranging applications in areas such as renewable energy, electric vehicles, and industrial automation. This paper aims to provide an in-depth exploration of the tools employed for comprehending power electronics technology. A notable aspect of this paper is its systematic approach, guiding students through a sequential progression of design, hardware implementation, and procedural steps for a series of power electronic circuits. Moreover, the paper elucidates the various tools, software, and their respective applications in the power electronics domain, contributing to a comprehensive understanding of the field. © 2023 IEEE.

关键词： Power electronics

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SpGCLF: A versatile deep graph contrastive learning framework for spatial transcriptomics analysis

SpGCLF: A versatile deep graph contrastive learning framewor...

引用

2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Xiang Yu, Junnan Li, Min Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

The rapid development of spatial transcriptomics (ST) has revolutionized the study of spatial heterogeneity and increased the demand for comprehensive methods to effectively characterize spatial domains. As a prerequisite for ST data analysis, spatial domain characterization is a critical step for downstream analysis and biological interpretation. Here, we propose a deep graph contrastive learning framework (spG-CLF) for spatial transcriptomics, which leverages contrastive learning through graph convolutional networks to effectively learn features among spots, addressing the issue of poor cross-platform generalizability. By alternately performing attribute and topological denoising, our method specifically reduces noise in ST data. We demonstrate that s p GCLF is effective for spatial domain identification, trajectory inference, multi-slice integration, and type annotation. © 2024 IEEE.

关键词： Contrastive Learning

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A Machine-Learning Approach for Children's Pain Assessments Using Prosodic and Spectral Acoustic Features

A Machine-Learning Approach for Children's Pain Assessments ...

引用

2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Jamal, Abdulrahman Al-Azani, Sadam SDAIA-KFUPM Joint Research Center for Ai King Fahd University of Petroleum & Minerals Information & Computer Science Department Dhahran Saudi Arabia King Fahd University of Petroleum & Minerals SDAIA-KFUPM Joint Research Center for Ai Dhahran Saudi Arabia

ISBN: (纸本)9798350322972

This paper presents a comprehensive study on the application of Artificial Intelligence (AI) techniques for classifying the reasons behind an infant's cry. The aim of the study is to develop an effective model that can accurately categorize infants' cries using Machine Learning (ML) and Deep Learning (DL) classification methods. The research focuses on two distinct datasets, namely DonateACry and BabyCry, in order to explore the best approach for cry classification. The study includes a preprocessing step with data augmentation techniques to address dataset imbalances. Two feature extraction methods are employed: audio spectrograms and a hybrid approach combining prosodic and spectral acoustic features. Different classification models, including K-Nearest Neighbors (K-NN), Dense Artificial Neural Networks (ANNs), and Convolutional Neural Networks (CNNs), are evaluated using the f1 score and accuracy. The results demonstrate that the hybrid approach of prosodic and spectral acoustic features outperforms audio spectrograms, pro-viding a more comprehensive representation of infants' cries and improving classification accuracy. The research showcases the potential of AI-driven advancements in revolutionizing childcare and parenting practices, contributing to improved well-being and care for infants. Overall, this study demonstrates the effectiveness of AI techniques in understanding infant communication and emphasizes their potential to improve care for infants. © 2023 IEEE.

关键词： Classification (of information)

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Motion Prediction Based on sEMG- Transformer for Lower Limb Exoskeleton Robot Control 8

Motion Prediction Based on sEMG- Transformer for Lower Limb ...

引用

8th IEEE International conference on Advanced Robotics and mechatronics, ICARM 2023

作者： Zeng, Min Gu, Jason Feng, Ying Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center AHU-IAI AI Joint Laboratory Hefei230601 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNS Canada College of Automation Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9798350300178

While lower limb exoskeleton robots can realize assisted walking by extracting the user's motion intention, it is difficult to effectively obtain the motion intention of the human body and convert it into information for the robot. In this paper, a novel model based on sEMG-Transformer is proposed for continuous motion prediction of multiple joint angles of lower limbs and applied to the developed lower limb exoskeleton robot. The sEMG-Transformer model can be used to extract the time series features from the sEMG sequences and establish the mapping between input data and multi-joint angle. Then, the predicted multi-joint angles are inputted into the developed lower limb exoskeleton robot. Experimental studies are performed with able-bodied human wearers and compared to existing methods, such as convolutional neural network (CNN), back propagation (BP), and long short-term memory (LSTM) networks. The motion intention estimation based on the sEMG-Transformer network has better estimation performance, which can effectively enable users to walk synchronously with the lower limb exoskeleton robot. © 2023 IEEE.

关键词： Backpropagation

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Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification

Self-Supervised Learning for Organs At Risk and Tumor Segmen...

引用

2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Isler, Ilkin Jha, Debesh Lisle, Curtis Rineer, Justin Kelly, Patrick Aydogan, Bulent Abazeed, Mohamed Turgut, Damla Bagci, Ulas University of Central Florida Department of Computer Science OrlandoFL United States Northwestern University Department of Radiology ChicagoIL United States Knowledge Vis Llc Altamonte SpringsFL United States Orlando Health Department of Radiation Oncology OrlandoFL United States University of Chicago Department of Radiation Oncology ChicagoIL United States Northwestern University Department of Radiation Oncology ChicagoIL United States

ISBN: (纸本)9798350322972

In this study, our goal is to show the impact of self-supervised pre-training of transformers for organ at risk (OAR) and tumor segmentation as compared to costly fully-supervised learning. The proposed algorithm is called Monte Carlo Transformer based U-Net (MC-Swin-U). Unlike many other available models, our approach presents uncertainty quantification with Monte Carlo dropout strategy while generating its voxel-wise prediction. We test and validate the proposed model on both public and one private datasets and evaluate the gross tumor volume (GTV) as well as nearby risky organs' boundaries. We show that self-supervised pre-training approach improves the segmentation scores significantly while providing additional benefits for avoiding large-scale annotation costs. © 2023 IEEE.

关键词： Supervised learning

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Research on Big Data Empowering the Internet of Things Data Collection in Smart Agriculture 8

Research on Big Data Empowering the Internet of Things Data ...

引用

8th IEEE Information Technology and mechatronics engineering conference, ITOEC 2025

作者： Wang, Hua Wang, Lingwei Zhu, Fuyu Huang, Zixuan Guangdong University of Science and Technology College of Computer Science Dongguan China

ISBN: (纸本)9798331517915

In the context of the digital economy era, smart agriculture represents a crucial direction for agricultural modernization, with its core lying in the utilization of information technologies such as big data, cloud computing, and the Internet of Things (IoT) to achieve intelligent and precise management of agricultural production. Guangdong Province, as a major agricultural province in China, has vigorously promoted the development of smart agriculture in recent years by implementing a series of policies and projects, such as the key special project on "Precision Agriculture and Ecological Green Technologies"and the "Action Plan for Digital Agricultural and Rural Development,"significantly enhancing the level of intelligence in agricultural production. Based on big data and IoT technologies, this study comprehensively analyzes the current development status, experimental results, and policy support for smart agriculture in Guangdong Province, aiming to explore how big data empowers IoT data acquisition in smart agriculture and thus contributes more significantly to agricultural modernization in Guangdong and even nationwide. The research results indicate that the system can effectively elevate the level of intelligence in agricultural production, reduce production costs, increase crop yields and quality, providing robust technical support for agricultural modernization. © 2025 IEEE.

关键词： Agricultural robots

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A study on the noise reduction method in turbid water environments using Spatial frequency characteristics 9

A study on the noise reduction method in turbid water enviro...

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9th International conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Koura, Ayumu Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Iizuka city Japan Hankyong National University School of ICT Robotics and Mechanical Engineering Iitc Anseong-si Korea Republic of

ISBN: (纸本)9798350363043

Scattering noise reduction is a challenging project to remove noise under scattering media conditions such as fog or turbid water. In previous study, blurring caused by scattering medium particles in fog or turbid water was removed by low-pass filtering. However, in this method, the accurate color information of the object may be lost due to rough low-frequency filtering. In addition, it is difficult to the obtain object shape information in heavy turbid environments. To solve this problem, we propose a new de-scattering algorithm that combines Fourier-domain filtering and integral imaging by creating a filter tailored to the frequency to be removed. To prove the proposed method, we perform a numerical analysis of experimental results using conventional and proposed methods. © 2024 IEEE.

关键词： Noise abatement

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Your Identity is Your Behavior - Continuous User Authentication based on Machine Learning and Touch Dynamics

Your Identity is Your Behavior - Continuous User Authenticat...

引用

2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Pelto, Brendan Vanamala, Mounika Dave, Rushit University of Wisconsin - Eau Claire Department of Computer Science Eau Claire United States Minnesota State University Mankato Department of Information Science Mankato United States

ISBN: (纸本)9798350322972

The aim of this undergraduate research paper is to investigate the use of continuous authentication with mobile touch dynamics, using three different algorithms: Neural Network, Extreme Gradient Boosting, and Support Vector Machine. Something that is starting to occur in the modern day is the use of mobile devices rising steadily, and in today's world, the number of mobile phone subscriptions has surpassed 6 billion [1]. What we will be looking into is the touch dynamics of users and how their interactions on a device can be utilized for authentication and security. The term touch dynamics refers to patterns from an individual, these patterns being completely unique, on a mobile device via the touch screen. These interactions could be swipes, taps, the amount of pressure on the screen, and several more. The use of this type of data expands well into continuous authentication for security. Continuous authentication refers to monitoring and verifying a user's identity on a device continuously, rather than just a login or a one-time security feature [2]. For this research to have enough data for algorithms to utilize, we collected touch data from 40 participants, since this is seen to be a good starting point [3],[4], using the LG V30+. The way this data was collected was through each participant playing 4 different mobile games, Minecraft, ***, PUBG, and *** for 10 minutes each game. Since these games are high-action games that utilize two-finger input, it allowed us to gather a lot more data from everyone. Once the data was collected and cleaned, the three algorithms stated before were then trained and tested, and their performance was evaluated based on their Accuracy, F1 Score, False Negative Rate, and False Positive Rate. The results from each of these models show that they were able to accurately classify unique users with an accuracy ranging from 75% to 95%, very similar to [5]. The SVC and XGBoost algorithms performed slightly behind the Neural Netwo

关键词： Dynamics

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Hands-on Activities for Learning computer Networks and Information Security in CS0 Course

Hands-on Activities for Learning Computer Networks and Infor...

引用

IEEE International conference on Teaching, Assessment and Learning for engineering (IEEE TALE)

作者： Nagataki, Hiroyuki Shirai, Shizuka Nishida, Tomohiro Ono, Atsushi Takemura, Haruo Osaka Univ Osaka Japan Osaka Gakuin Univ Osaka Japan Senri Kinran Univ Osaka Japan

ISBN: (纸本)9781665453318

This paper reports the lesson design of theory and technology of computer networks and information security, designed for the informatics course for non-CS major university students. The lesson unit consists of two lessons applied by flipped learning style, in which students first watch on-demand video lectures to acquire basic knowledge of network theory and technology, then they do hands-on activities using some web tools that we developed to consolidate the knowledge. The questionnaire results after the CS0 course where the lesson design was applied suggested that the hands-on activity was effective in improving the understanding of network and security.

关键词： introductory computer science education active learning flipped learning computer network information security hands-on activities

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A Way of Safe Wireless Networks using IμUWB/Vision through Sensor Networks 4

A Way of Safe Wireless Networks using IμUWB/Vision through ...

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4th IEEE International conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Sharmila, P. Maheswaran, M. Mohanraj, T. Verma, Rajan Malviya, Bhupendra Subbarao, S.P.V. Prince Shri Venkateshwara Padmavathy Engineering College S & H Dept TN India Nehru Institute of Engineering and Technology Department of Mechatronics Engineering Tamil Nadu T.M.Palayam India Karpagam Academy of Higher Education Department of Computer Science and Engineering TN India Chitkara University Chitkara Center for Research and Development Himachal Pradesh India M.P Bhopal India Sreenidhi Institute of Science and Technology Electronics and Communication Engineering Department Hyderabad India

ISBN: (纸本)9798350360165

In current military and private spheres, the demand for exact localization within the home of mobile agents, especially small unmanned aircraft (UAVs), has attracted significant attention. This paper shows a new method to indoor UAV localization using Ultra-WideBand technology, combined with a cost-effective Motion Measurement Unit (IMU) while vision-based devices. A vital improvement to the positioning process is brought through the inclusion of an additional Kalman filter (EKF). The utilization of an inexpensive and low-cost IMU with the modeling phase, paired with the inclusion of vision odometer measurements for identifying marks in the closeness of the landing area, forms a key aspect of this method. The addition of range data plays a key role in reducing mistakes affiliated with the limited capabilities of gyroscopes and accelerometers, which are inherent in inertial devices. By deploying these multi-modal technologies that the suggested approach achieves a surprising level of precision, with data showing an amazing precision equal to 10 centimeters. This development holds potential for improving the operating effectiveness of small UAVs in indoor settings, offering a robust solution that joins Ultra-WideBand technology, IMU, while vision-based sensors throughout the framework of an Extension Kalman Filter. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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