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Dynamic Modeling and Analysis of Impact-Resilient MAVs Undergoing High-Speed and Large-Angle Collisions with the Environment

Dynamic Modeling and Analysis of Impact-Resilient MAVs Under...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zhichao Liu Konstantinos Karydis Dept. of Electrical and Computer Engineering University of California Riverside

Micro Aerial Vehicles (MAVs) often face a high risk of collision during autonomous flight, particularly in cluttered and unstructured environments. To mitigate the collision impact on sensitive onboard devices, resilient MAVs with mechanical protective cages and reinforced frames are commonly used. However, compliant and impact-resilient MAVs offer a promising alternative by reducing the potential damage caused by impacts. In this study, we present novel findings on the impact-resilient capabilities of MAVs equipped with passive springs in their compliant arms. We analyze the effect of compliance through dynamic modeling and demonstrate that the inclusion of passive springs enhances impact resilience. The impact resilience is extensively tested to stabilize the MAV following wall collisions under high-speed and large-angle conditions. Additionally, we provide comprehensive comparisons with rigid MAVs to better determine the tradeoffs in flight by embedding compliance onto the robot's frame.

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Prostate Cancer Detection Using Integrated Multi Modal Approaches

Prostate Cancer Detection Using Integrated Multi Modal Appro...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Rao, Manjula Gururaj Priyanka, H. Ahamed Shafeeq, B.M. Hemant Kumar Reddy, K. Nmam Institute of Technology Depatment of Computer Science and Engineering Nitte India Pes University Bengaluru India Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Cse Manipal India Vit Ap University Dept. of Cse Inavolu India

ISBN: (纸本)9798350375442

Cancer is one of the terminal diseases that significantly affects an individual's health. Tumours are created when aberrant cells proliferate and divide out of control, posing a threat to neighbouring tissues and organs. Early detection through regular screenings and check-ups can make cancer more treatable. Lots of people throughout are impacted by prostate cancer, the cancer in males that is most frequently diagnosed. The proposed model is a multimodal hybrid approach that utilizes data *** files containing prostate attributes and prostate images. It employs both ML and DL models to classify the data as cancerous or non-cancerous and can further stage the cancerous cells into four stages. The proposed method uses the majority voting system to identify the cancerous or non-cancerous. The model achieves an overall accuracy of 81.48 %, with the ML model at 77.8% and the DL model at 87%. © 2024 IEEE.

关键词： Lung cancer

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VOID - An Assistant (Chatbot) to Education Queries System

VOID - An Assistant (Chatbot) to Education Queries System

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2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Chakraborty, Debaissh Mishra, Anjana Kumar, Vikash Singh, Saloni Hani, Harshita C.V. Raman Global University Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9781665461092

Chatbots are the future of technology and it has become a basic requirement of various industries. Chatbots are designed to interact with human-like humans with the help of AI-based technology. Every industry cannot afford man-power to interact with clients and customers 24×7 so that chatbot has come into play which does the same work with no extra cost. It is a new and emerging technology that is in high demand across a wide range of businesses. It is used to simulate a human like conversation and a machine to relieve the workload. It can be easily deployed in any field where products must be developed and evaluated before being released. During the admission time in college, reception is packed, and people have to wait for their turn, and this entire process takes too much time. Although every college has their own official website, but everyone cannot find the appropriate answer of their query. The objective of this paper is to reduce the workload by developing an interactive environment between a human and a machine that gives a reply to the query of the user on behalf of a human for the education system. © 2022 IEEE.

关键词： Query processing

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Enhancing 3D Modeling Quality Through Orthorectified Image Accumulation

Enhancing 3D Modeling Quality Through Orthorectified Image A...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Cheolsu Kwag Euntaek Kim Yongsuk Lee Seungmin Jung Sung Soo Hwang Hwangryol Ryu Youngmin Moon Dept. of Computer Science and Electrical Engineering Handong Global University Pohang Republic of Korea School of Computer Science and Electrical Engineering Handong Global University Pohang Republic of Korea Smart Engineering Research Laboratory Research Institute of Industrial Science & Technology Pohang Republic of Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This study introduces a novel methodology designed to facilitate the capture of comprehensive image datasets, crucial for accurate 3D modeling of expansive indoor spaces. Leveraging orthophotos generated from panoramic camera data and visualizing image coverage density through heatmaps, our approach effectively identifies under-captured and unvisited areas. This enables targeted image acquisition, minimizes redundant reshoots, and enhances the overall quality and fidelity of 3D models.

关键词： Heating systems Solid modeling Three-dimensional displays Accuracy Data visualization Minimization Cameras

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On the Detection of Spectrum Irregularities through Deep Learning in Dense IoT architectures

On the Detection of Spectrum Irregularities through Deep Lea...

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IEEE Conference on Standards for Communications and Networking (CSCN)

作者： Katerina Kyriakou Kostas Chounos Thanasis Korakis Dept. of Electrical and Computer Engineering University of Thessaly Greece

In this work, a novel Deep Learning model that combines Q Learning and Neural Networks is proposed and experimentally evaluated. The proposed scheme is developed in order to detect and tackle the effects of spectrum anomalies, which are unexpectedly appeared in ultra-dense Internet of Things (IoT) architectures. The protocol which is taken into consideration in this work, is the IEEE 802.11ah (Wi-Fi HaLow) and it is tested under strong interference, caused between wireless links which transmit in non-overlapping frequencies. The proposed approach trains a model that constantly observes the wireless environment and obtains an optimal policy for the transmission time parameter re-configurations of the participating devices. The experimental evaluation showcases that the proper training of the proposed Deep Q Learning model, leads to remarkable increased Packet Delivery Ratio (PDR) and throughput in the examined scenarios. Apart from the improvements (+35%) observed on a PDR and throughput basis, the proposed algorithm also achieves overall higher channel utilization, increased transmission opportunities, and fairness in terms of channel access.

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Coding of Multi-Focused Plenoptic Image using Disparity Shift and Sharpness-Aware Constraints

Coding of Multi-Focused Plenoptic Image using Disparity Shif...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Thuc Nguyen Huu Vinh Van Duong Jonghoon Yim Byeungwoo Jeon Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea

Multi-focused plenoptic images possess many special characteristics related to the micro-images (MIs) array, which are expected to be useful in further increasing its compression performance. Those special characteristics come from the much overlap and sharpness variance among its micro-images, and proper handling of such properties can lead to better patch-based prediction. In this paper, for multi-focused plenoptic image data, we design a new prediction model taking into account the disparity shift constraint coming from the overlaps and the sharpness variation. Experiment results show coding gain respectively of 21% over the HEVC Intra and 27% when the proposed method is combined with the Intra Block Copy (IBC) tool which is reported very effective in plenoptic image coding.

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Optimization Of Fuzzy Logic-Based Clustering For Wireless Sensor Networks 2

Optimization Of Fuzzy Logic-Based Clustering For Wireless Se...

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2nd International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Hussein, Ahmed Mohammed Kurnaz, Sefer Saad, Mohammed Ayad Ibrahim, Raed Khalid Ali, Adnan Hussein Taher, Anmar Yahya Altinbas University Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept. Turkey Al-Kitab University Dept. of Medical Instrumentations Technique Engineering Kirkuk36001 Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad Iraq Institute of Technology Middle Technical University Electrical Techniques Department Baghdad Iraq

ISBN: (纸本)9798350373363

A strong framework for managing complicated and uncertain data patterns is provided by fuzzy C-Means (FCM) clustering, a potent and frequently used data analysis technique that is adaptable for data clustering in a variety of applications, including pattern recognition, picture segmentation, and data mining. Assigning data points to various clusters with different degrees of membership, in contrast to typical hard clustering approaches, makes FCM especially well-suited for situations where data show ambiguity and overlap. An overview of FCM is given in this abstract, emphasizing its fundamental ideas, iterative optimization procedure, and capacity to manage intricate data patterns. It also goes over the benefits and uses of FCM in practical situations, highlighting how it can be used to provide cluster assignments that are both flexible and nuanced. For researchers and practitioners looking for efficient ways to cluster data even in the face of ambiguity and incomplete memberships, FCM remains a useful tool. © 2023 IEEE.

关键词： Pattern recognition

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MILAB at PragTag-2023: Enhancing Cross-Domain Generalization through Data Augmentation with Reduced Uncertainty 10

MILAB at PragTag-2023: Enhancing Cross-Domain Generalization...

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10th Workshop on Argument Mining, ArgMining 2023

作者： Lee, Yoonsang Lee, Dongryeol Jung, Kyomin College of Liberal Studies Seoul National University Korea Republic of Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of ASRI Seoul National University Korea Republic of

ISBN: (纸本)9798891760509

This paper describes our submission to the PragTag task, which aims to categorize each sentence from peer reviews into one of the six distinct pragmatic tags. The task consists of three conditions: full, low, and zero, each distinguished by the number of training data and further categorized into five distinct domains. The main challenge of this task is the domain shift, which is exacerbated by nonuniform distribution and the limited availability of data across the six pragmatic tags and their respective domains. To address this issue, we predominantly employ two data augmentation techniques designed to mitigate data imbalance and scarcity: pseudo-labeling and synonym generation. We experimentally demonstrate the effectiveness of our approaches, achieving the first rank under the zero condition and the third in the full and low conditions. © 2023 Association for Computational Linguistics.

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A Hybrid Algorithm to Dual Sparse Sampling Measurement in Time-Resolved Electromagnetic Near-Field Scanning

A Hybrid Algorithm to Dual Sparse Sampling Measurement in Ti...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Yanming Zhang Steven Gao Lijun Jiang Dept. of Electronic Engineering Chinese University of Hong Kong Hong Kong Dept. of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

ISBN: (数字)9798350360394

ISBN: (纸本)9798350360400

Time-resolved electromagnetic near-field scanning is vital for antenna measurement and addressing complex electromagnetic interference and compatibility issues. However, the swift acquisition of high-resolution spatiotemporal data remains challenging due to physical constraints, such as moving the probe position and allowing sufficient time for sampling. This paper introduces a novel hybrid approach that combines Kriging for sparse spatial measurement, compressed sensing (CS) for sparse temporal sampling, and dynamic mode decomposition (DMD) for a comprehensive analysis of dual-sparse sampling electromagnetic near-field data. CS optimizes sparse sampling in the time domain, capitalizing on the inherent sparsity within electromagnetic radiated signals, resulting in reliable representation of time-domain signals and reducing the required time samples. Latin hypercube sampling guides the probe position, facilitating sparse measurement in the space domain. DMD extracts meaningful insights from the resulting sparse spatiotemporal data, producing sparse dynamic modes and temporal evolution information. Subsequently, Kriging is employed to infer missing spatial measurements for each sparse dynamic mode. Finally, the entire spatiotemporal signals are reconstructed based on interpolated dynamic modes and temporal evolution information. Validation of the proposed method is demonstrated with an example using crossed dipole antennas as the device under test. The Kriging-CS-DMD framework effectively reconstructs electromagnetic fields with precision while concurrently reducing the measurement workload in both the time and space domains. This methodology holds promise for various applications, including space-time-modulated electronic devices.

关键词： Antenna measurements Time measurement Spatiotemporal phenomena Sensors Electromagnetic fields Data mining Electromagnetics Time-domain analysis Probes Compressed sensing

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Psychological Wellbeingness Prediction Using Machine Learning with Hybrid Hyparameter Tuning Techniques

Psychological Wellbeingness Prediction Using Machine Learnin...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Puvanenthira Rajah Masruk Habib Ravikumar R N Sivakumar Nadarajan Bhagyashree Amit Chougule Kirubhakaran M Dept. of Computer Engineering Marwadi University Rajkot India Dept. of Computer Science and Engineering Alliance University Bangalore India Dept. of Computer Engineering Tatyasaheb Kore Institute of Engineering and Technology Maharashtra India Dept. of AI & DS Suriya Engineering College Erode India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

This study investigates the application of machine learning (ML) techniques in predicting Psychological Well-being outcomes, emphasizing the use of ensemble methods like AdaBoost and Random Forest for enhanced accuracy. With Psychological well-being issues affecting a significant portion of the global population, traditional assessment methods are challenged by issues of accessibility, stigma, and subjectivity. Leveraging data from various surveys, this research compares different ML algorithms, including Logistic Regression and Support Vector Machines, in predicting psychological well-being conditions. The results highlight the superior performance of AdaBoost and Random Forest, particularly when hyperparameters are finely tuned, achieving accuracies up to 0.996. The study underscores the potential of ML in improving psychological well-being interventions by offering precise, unbiased, and accessible diagnostic tools. Future research directions include the integration of multimodal data and the development of models that can suggest personalized treatment plans while ensuring data privacy. This paper advocates for the integration of advanced computational techniques with clinical insights to revolutionize psychological well-being diagnostics and care.

关键词： Adaptation models Accuracy Computational modeling Medical treatment Mental health Predictive models Prediction algorithms Ensemble learning Tuning Random forests

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