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1st IEEE International Conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Satyanrayana, Madgula Veeramsetty, Venkataramana Rajababu, Durgam SR University Department of Electrical and Electronics Engineering Telangana Warangal506371 India School of Computer Science and Artificial Intelligence SR University Center for AI and Deep Learning Telangana Warangal506371 India

ISBN: (纸本)9798331542108

The present power system have been charac-terized by high dynamics due to advances such as the accessorial loads, switching power supply equipment and enhancement of power system regulations. The power quality (PQ) analysis was earlier performed using Fourier Transform (FT) where parameters such as time-amplitude variations and frequency-amplitude variations were used. However, these parameters focus on the stationary power quality disturbances(PQDs) and cannot capture non-stationary or short duration PQ disturbances. Modern power systems need a broader viewpoint based on the amplitude, duration, and frequency of interruptions. In this paper, Short Time Fourier Transform(STFT) is proposed for identification of short duration PQDs which include voltage sags, swells, interruptions, transient oscillations and harmonic. In this paper, power quality disturbances will be assessed by Short-Time Fourier Transform which enhances PQ analysis under dynamic conditions. © 2025 IEEE.

关键词： Wavelet transforms

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Groundbreaking taxonomy of metaverse characteristics

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Artificial Intelligence Review 2025年第8期58卷 1-50页

作者： Sadeghi-Niaraki, Abolghasem Rahimi, Fatema Azlan, Nur Alya Emanuelle Binti Song, Houbing Ali, Farman Choi, Soo-Mi Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea Republic of BaltimoreMD21250 United States Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

The Metaverse, a dynamic and immersive virtual realm, has captured the imagination of researchers and enthusiasts worldwide. This survey paper aims to introduce a groundbreaking taxonomy for the characteristics of the Metaverse offering a structured and adaptable framework that extends beyond existing categorizations by incorporating dynamic transformations. Unlike prior taxonomies, which often focus on fixed attributes, our approach emphasizes the dynamic evolution of Metaverse characteristics. Through an extensive review of published literature, this study explores key technological, social, economic, and ethical dimensions of the Metaverse. It introduces a process-oriented classification based on 23 distinct characteristics, including immersification, spatiotemporalification, interactification, persistentification, presentification, personification, unification, imaginification, economification, uncertaintification, and credification. By mapping these evolving aspects, we provide a structured and future-proof foundation for understanding the Metaverse’s continuous development. This survey establishes a new standard for comprehensiveness and innovation, shedding light on the diverse facets that have been explored in literature. Through this novel taxonomy, we provide a detailed map of the current landscape and offer insights that pave the way for future research and development in this burgeoning digital frontier. © The Author(s) 2025.

关键词： Comprehensive analysis Futuristic exploration Metaverse characteristics Paradigm-shifting taxonomy Revolutionary categorization

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Opportunistic Network Routing Strategy Based On Node Sleep Mechanism

Opportunistic Network Routing Strategy Based On Node Sleep M...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Zhuoyuan Li Danjie Bao Xiaodong Xu Feng Zhang Gang Xu College of Computer Science Inner Mongolia University Hohhot China The Engineering Research Center of Ecological Big Data Ministry of Education China School of Computer and Information Technology Shanxi University Taiyuan China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Opportunity networks are wireless AD-hoc networks deployed in complex and harsh environments. To mitigate energy loss in opportunistic network routing, we propose a model called Nodes Sleep Scheduling based on Q-learning (NSQ) by combining the node sleep mechanism with classical opportunistic network routing algorithms when the nodes in the network have sufficient buffer space. In the NSQ model, the node sleep scheduling process is modeled as a Markov decision process, the corresponding state set, action set and reward function are defined, and the Q-learning algorithm iterates the value function. The model continuously iterates and converges through the self-learning process of the value function in the nodes, autonomously learns, and ultimately obtains an optimal sleep scheduling strategy. The experimental results show that the NSQ model can effectively reduce nodes' energy consumption and has certain self-learning abilities compared with other node sleep scheduling strategies.

关键词： Wireless communication Energy consumption Q-learning Processor scheduling Markov decision processes Software algorithms Routing

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DiLiGenT-Π: Photometric Stereo for Planar Surfaces with Rich Details – Benchmark dataset and Beyond

DiLiGenT-Π: Photometric Stereo for Planar Surfaces with Ric...

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International Conference on computer Vision (ICCV)

作者： Feishi Wang Jieji Ren Heng Guo Mingjun Ren Boxin Shi School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University School of Computer Science National Engineering Research Center of Visual Technology Peking University School of Computer Science AI Innovation Center Peking University School of Mechanical Engineering Shanghai Jiao Tong University School of Artificial Intelligence Beijing University of Posts and Telecommunications Osaka University

Photometric stereo aims to recover detailed surface shapes from images captured under varying illuminations. However, existing real-world datasets primarily focus on evaluating photometric stereo for general non-Lambertian reflectances and feature bulgy shapes that have a certain height. As shape detail recovery is the key strength of photometric stereo over other 3D reconstruction techniques, and the near-planar surfaces widely exist in cultural relics and manufacturing workpieces, we present a new real-world dataset DiLiGenT-Π containing 30 near-planar scenes with rich surface details. This dataset enables us to evaluate recent photometric stereo methods specifically for their ability to estimate shape details under diverse materials and to identify open problems such as near-planar surface normal estimation from uncalibrated photometric stereo and surface detail recovery for translucent materials. To inspire future research, this dataset will open soruced at https://***/***.

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CNN segmentation of skin melanoma in pre-processed dermoscopy images 2

CNN segmentation of skin melanoma in pre-processed dermoscop...

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2nd International Conference on Machine Learning and data engineering, ICMLDE 2023

作者： Kadry, Seifedine Verdú, Elena Damasevicius, Robertas Abualigah, Laith Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu's thresholding, (iii) Kapur's thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Reliable Sound Re-labeling Approach for Bird Sound Classification 27

Reliable Sound Re-labeling Approach for Bird Sound Classific...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Van Truong, Hoang Nghia, N.V.N. To, Alex Le, Duc-Trong Nguyen, Hoang D. AI Center FPT Software Company Limited Ha Noi100000 Viet Nam Pintel Ltd Seoul Korea Republic of Computer Science The University of Sydney CamperdownNSW2006 Australia University of Engineering and Technology Vietnam National University Hanoi Viet Nam School of Computer Science and Information Technology University College Cork CorkT12 XF62 Ireland

Passive acoustic monitoring has become an effective and economically scalable solution for wildlife monitoring in recent years, especially for population monitoring of endangered birds in hardly accessible and high-elevation areas. As sound recorders are deployed on the field for extended periods (months), continuous sound streams are often complex in nature, with noises and intermittent signals. Generally, it is prohibitively expensive to label bird sounds with the exact onset and offset time, thus, during training, the data is often provided with only presence/absence labeling (weak labeling) that states which bird species are present in each long recording without temporal information. During test time, it is, however, desirable to provide fine-grained detection in short audio segments. To bridge the gap between the difference of weak labels used for training and strong labels used for testing, we propose a re-labeling approach with two stages: (1) we train a model with weak labeling;and (2) using the model obtained from stage 1, we generate labels for short audio segments and retrain the model on short audio segments with the newly generated labels. We applied our approach in both classification and sound event detection and achieved consistently good performance across multiple random seeds. In BirdCLEF 2022, our model ranked in the top 1.1%, the 9th best entry out of the total 807 entries in the competition. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Endangered Bird Classification Re-labeling Reliable Machine Learning

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions 2

COVID19 detection in chest x-ray using vision-transformer wi...

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2nd International Conference on Machine Learning and data engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, Laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

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Automatic liver-vessel examination from CT slice using Kapur's thresholding and watershed algorithm 2

Automatic liver-vessel examination from CT slice using Kapur...

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2nd International Conference on Machine Learning and data engineering, ICMLDE 2023

computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur's tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme. © 2024 Elsevier B.V.. All rights reserved.

关键词： Medical imaging

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Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Huang, Kaiwen Zhou, Tao Fu, Huazhu Zhang, Yizhe Zhou, Yi Gong, Chen Liang, Dong The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Institute of High-Performance Computing Agency for Science Technology and Research Singapore The School of Computer Science and Engineering Southeast University Nanjing211189 China The Lauterbur Research Center for Biomedical Imaging The Research Center for Medical AI Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmentation, such as the Segment Anything Model (SAM), have revealed robust generalization capabilities. However, applying these models directly to medical image segmentation still exposes performance degradation. In this paper, we propose a learnable prompting SAM-induced Knowledge distillation framework (KnowSAM) for semi-supervised medical image segmentation. Firstly, we propose a Multi-view Co-training (MC) strategy that employs two distinct sub-networks to employ a co-teaching paradigm, resulting in more robust outcomes. Secondly, we present a Learnable Prompt Strategy (LPS) to dynamically produce dense prompts and integrate an adapter to fine-tune SAM specifically for medical image segmentation tasks. Moreover, we propose SAM-induced Knowledge Distillation (SKD) to transfer useful knowledge from SAM to two sub-networks, enabling them to learn from SAM’s predictions and alleviate the effects of incorrect pseudo-labels during training. Notably, the predictions generated by our subnets are used to produce mask prompts for SAM, facilitating effective inter-module information exchange. Extensive experimental results on various medical segmentation tasks demonstrate that our model outperforms the state-of-the-art semi-supervised segmentation approaches. Crucially, our SAM distillation framework can be seamlessly integrated into other semi-supervised segmentation methods to enhance performance. The code will be released upon acceptance of this manuscript at https://***/taozh2017/KnowSAM. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Validity Matters: Uncertainty-Guided Testing of Deep Neural Networks

Validity Matters: Uncertainty-Guided Testing of Deep Neural ...

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作者： Jiang, Zhouxian Li, Honghui Wang, Rui Tian, Xuetao Liang, Ci Yan, Fei Zhang, Junwen Liu, Zhen School of Computer Science and Technology Beijing Jiaotong University Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China Faculty of Psychology Beijing Normal University Beijing China School of Transportation Science and Engineering Harbin Institute of Technology Harbin China School of Automation and Intelligence Beijing Jiaotong University Beijing China

Despite numerous applications of deep learning technologies on critical tasks in various domains, advanced deep neural networks (DNNs) face persistent safety and security challenges, such as the overconfidence in predicting out-of-distribution samples and susceptibility to adversarial examples. Thorough testing by exploring the input space serves as a key strategy to ensure their robustness and trustworthiness of these networks. However, existing testing methods focus on disclosing more erroneous model behaviours, overlooking the validity of the generated test inputs. To mitigate this issue, we investigate devising valid test input generation method for DNNs from a predictive uncertainty perspective. Through a large-scale empirical study across 11 predictive uncertainty metrics for DNNs, we explore the correlation between validity and uncertainty of test inputs. Our findings reveal that the predictive entropy-based and ensemble-based uncertainty metrics effectively characterize the input validity demonstration. Building on these insights, we introduce UCTest, an uncertainty-guided deep learning testing approach, to efficiently generate valid and authentic test inputs. We formulate a joint optimization objective: to uncover the model's misbehaviours by maximizing the loss function and concurrently generate valid test input by minimizing uncertainty. Extensive experiments demonstrate that our approach outperforms the current testing methods in generating valid test inputs. Furthermore, incorporating natural variation through data augmentation techniques into UCTest effectively boosts the diversity of generated test inputs. © 2024 John Wiley & Sons Ltd.

关键词： Adversarial machine learning

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