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A New Comparative Approach Based on Features of Subcomponents and Machine Learning Algorithms to Detect and Classify Power Quality Disturbances

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Electric Power Components and systems 2024年第8期52卷 1269-1292页

作者： Akkaya, Sıtkı Yüksek, Emre Akgün, Hasan Metehan Department of Electrical and Electronics Engineering of Faculty of Engineering and Natural Sciences Sivas University of Science and Technology Sivas Turkey Department of Computer Engineering of Faculty of Engineering and Natural Sciences Sivas University of Science and Technology Sivas Turkey Department of Aircraft Technology of Sivas Vocational School Sivas University of Science and Technology Sivas Turkey

Current measurement systems based on the IEEE-1159 standard have some limitations and robustness problems under noisy and fast-changing conditions. Besides, applying different methods for each Power Quality Disturbance (PQD) to every window is required but time-consuming and not feasible. Therefore, different kinds of two-stage methods, Detection and Classification (D&C), have been improved in many studies. Then, the required measurement can be performed to define disturbance. For this purpose, a new approach based on features of subcomponents with Machine Learning Algorithms (MLAs) to detect and classify PQDs is proposed. 21-class dataset including single and multiple PQDs under different noisy conditions was prepared randomly. Of this dataset, determined features were extracted and some of these were selected. Then, selected features were trained and tested with some MLAs in a workstation. Results obtained from comparative MLAs and the other classification methods show that the best MLA with related features is Random Forest with 96.97% while LightGBM, k-Nearest Neighbors, and XGBoost 96.85%, 96.73%, and 92.82% accuracy, respectively. Because the selected features, optimized parameters, and the related MLA were obtained by investigating for features provided from the PQDs in the whole parameter space, this approach brings the advantages of high accuracy, low D&C complexity, and computing load. © 2023 Taylor & Francis Group, LLC.

关键词： Power quality

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Identification and Acknowledgment of Programmed Traffic Sign Utilizing Profound Convolutional Neural Organization

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Intelligent Automation & Soft Computing 2023年第2期35卷 1527-1543页

作者： P.Vigneshwaran N.Prasath M.Islabudeen A.Arunand A.K.Sampath Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering School of EngineeringPresidency UniversityBengaluru560064India

Trafﬁc signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and indivi-duals by walking,*** a segment of the clever transportation structure,the acknowledgment of trafﬁc signs is basic for the driving assistance system,trafﬁc sign upkeep,self-administering driving,and various *** are different assessments turns out achieved for trafﬁc sign acknowledgment in the ***,most of the works are only for explicit arrangements of trafﬁc signs,for example,beyond what many would consider a possible ***ﬁc sign recognizable proof is generally seen as trying on account of various complexities,for example,extended establishments of trafﬁc sign *** critical issues exist during the time spent identiﬁcation(ID)and afﬁrmation of trafﬁc *** signs are occasionally blocked not entirely by various vehicles and various articles are accessible in busy time gridlock scenes which make the signed acknowledgment hard and walkers,various vehicles,constructions,and loads up may frustrate the ID structure by plans like that of road *** concealing information from trafﬁc scene pictures is affected by moving light achieved by environment conditions,time(day-night),and ***ﬁc sign revelation and afﬁrmation structure has two guideline sorts out:The essential stage incorpo-rates the trafﬁc sign limitation and the resulting stage portrays the perceived trafﬁc signs into a particular class.

关键词： Trafﬁc sign classiﬁer convolution neural network image vehicle

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Identification and Classification of Medicinal Plants using Machine Learning Algorithms 3

Identification and Classification of Medicinal Plants using ...

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

作者： Bhargavi, G. Jayaraman, Venkatesh Jabez, J. Deepa, R. Surendran, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily accessible and credible information on plant categorization, particularly focusing on medicinal plants. This innovative project synergizes machine learning capabilities, botanical proficiency, and user-friendly technology to give users the insights required to harness the benefits of medicinal plants responsibly and sustainably. The platform facilitates precise categorization and a comprehensive understanding of the therapeutic attributes of plants. © 2025 IEEE.

关键词： Medicinal chemistry

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Energy-Efficient Scheduling and Optimization in Edge Computing based on Deep Q-Networks 15

Energy-Efficient Scheduling and Optimization in Edge Computi...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Madiyev, Askar Bulegenov, Daulet Karzhaubayev, Anuar Murzabulatov, Meiram Bui, Dinh Mao School of Engineering and Digital Sciences Nazarbayev University Department of Computer Science Astana Kazakhstan

ISBN: (纸本)9798331507695

Mobile Edge Computing (MEC) became a promising paradigm that provides computational and storage capabilities for enhancing user devices that run heavy mobile applications. MEC brings computational resources closer to the network's edge, reducing latency and bandwidth consumption by processing data without transporting computational tasks to distant cloud servers. Reduced latency, guaranteed by MEC because of the geographical proximity, allows improved quality of service (QoS). Although MEC solves the problem of limited computational capabilities of user devices, this computing paradigm requires a strategic approach to manage power consumption, especially in contemporary MEC environments that are characterized by dynamic client workloads on the system, increasing the complexity of the problem. In order to deal with this issue, we proposed a Deep Q-Network (DQN)-based offloading technique to reduce power consumption with such dynamic workloads considerably. This work also highlighted the potential of reinforcement learning in dynamic and heterogeneous MEC environments. © 2025 IEEE.

关键词： Mobile edge computing

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Advanced Plant Species Classification with Interclass Similarity using a Fine-Grained computer Vision Approach

Advanced Plant Species Classification with Interclass Simila...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Suchithra Dhruthi, P.A. Somashekhar, B.M. Rani, N. Shobha Lin, Hong Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science Mysore India Gitam School Of Technology Gitam University Department Of Computer Science And Engineering Bengaluru India University Of Houston Department Of Computer Science And Engineering Technology Houston United States

ISBN: (纸本)9798350371567

Plant species classification holds immense importance across diverse sectors such as agriculture, environmental conservation, and botanical research. Accurate identification of plant species is fundamental for effective ecological management, agricultural practices, and biodiversity preservation. In this context, the research addresses the critical task of fine-grained plant classification, specifically emphasizing vegetable and fruit plants. Recognizing the significance of visual similarity analysis in plant sciences, a robust methodology leveraging Convolutional Neural Networks (CNN) is introduced. The hybrid CNN model combines the interpretability of VGG16 with the gradient-stabilizing benefits of ResNet50's residual connections, enabling nuanced classification of 23 plant classes. Achieving an outstanding 99% accuracy in plant class prediction, the model is not only highly accurate but also interpretable. Additionally, the incorporation of cosine similarity enhances the robustness of the approach, facilitating a leaf similarity search application. This innovative feature strengthens the identification of visually similar plant images based on extracted features, significantly contributing to the advancement of plant species classification methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Unveiling the invisible struggles: Exploring student perspectives on mental health in universities

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IEEE Potentials 2024年第3期43卷 31-35页

作者： Murali, Ritwik Avudaiappan, Veeramanohar Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Coimbatore Coimbatore641112 India Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Amrita School of Engineering Coimbatore Coimbatore641112 India

In the current academic environment, the increasing prevalence of stress among students has become a critical concern, significantly affecting both mental health and academic outcomes. Factors such as heavy academic workloads, interpersonal conflicts, financial challenges, and societal pressures contribute to cognitive impairments, procrastination, and declining academic performance. Unaddressed, this stress can escalate into severe mental health conditions, including anxiety, depression, and self-harming tendencies, with marginalized or overwhelmed students being particularly vulnerable. Familial expectations, financial instability, imposter syndrome, fear of missing out, cyberbullying, and unsupportive institutional cultures further intensify these challenges. While mental health support mechanisms exist within institutions, they often remain reactive, addressing issues only after they manifest. To address this growing crisis, a shift toward proactive measures is essential, encompassing counseling, meditation, peer support systems, and engagement in extracurricular activities to foster emotional resilience. A holistic and preventative approach that emphasizes early intervention, open communication, and supportive academic environments is imperative to promote students' well-being and academic success. © 1988-2012 IEEE.

关键词： Students

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Deep Convolutional Neural Network with Artificial Bee Colony Optimization For Plant Disease Detection and Classification 2

Deep Convolutional Neural Network with Artificial Bee Colony...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Anbazhagan, K. Eluri, Nageswara Rao Toomula, Srilatha Brindha, T.V. Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Andhra Pradesh Guntur India Rbvrr Women's' College Department of Computer Science and Engineering Hyderabad India Sri Venkateshwara College of Engineering and Tecyhnology Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350384277

Plant diseases pose severe risks to agricultural production and global food security. For prompt intervention and mitigation, early and precise disease detection is crucial. Due to the development of trustworthy computer vision and machine learning technologies, automated plant disease detection systems are currently considered a potential solution to this issue. In this study, we provide a unique Deep Convolutional Neural Networks (DCNN)-based method for diagnosing plant illnesses. Our DCNN model automatically extracts pertinent features from plant photos, making it possible to classify healthy and unhealthy plants accurately. The dataset used in this study comprises a sizable collection of images with labels that show different plant species affected by various diseases. To improve its generalization and robustness to changing environmental circumstances and imaging artifacts, the proposed model is trained utilizing a mix of data augmentation via Haralick Texture. The suggested method uses a deep CNN to extract high-level information from photos of plants, allowing it to recognize subtle patterns suggestive of various diseases. The CNN architecture has been extensively crafted and adjusted to achieve the best accuracy and efficiency. The CNN's hyperparameters are optimized using the ABCO (Artificial Bee Colony Optimization) approach to increase classification accuracy further. ABCO mimics honeybee foraging to explore the hyperparameter space and find the best parameters efficiently. This improves the DCNN-ABCO performance. The findings demonstrate high accuracy, precision, and recall across various illness classes, demonstrating the model's dependability for practical applications and its capacity to identify previously unidentified disease patterns. © 2024 IEEE.

关键词： Convolutional neural networks

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Physics-Based 2D Direction Finding of an Amplitude Modulated Signal Using a Uniform Triangular Array

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Progress in Electromagnetics Research C 2025年 155卷 121-126页

作者： Ren, Kai Department of Electrical Engineering and Computer Science South Dakota School of Mines and Technology Rapid CitySD57701 United States

A uniform triangular array (UTA) is proposed for physics-based 2D direction-of-arrival (DOA) estimations of unknown incoming signals. Three capacitively loaded top-hat antennas are used as array elements. Unlike conventional array-based direction finding (DF) systems, complex antenna radiation patterns are used in array manifold calculations and DOA predictions, where coupling among array elements is naturally resolved. Both continuous wave (CW) signals and single tone signals with amplitude modulation (AM) are considered in DF simulations. Cramer-Rao bound (CRB) values are calculated to provide theoretical lower bounds of DF accuracies. Multiple signal classification (MUSIC) algorithm is used to further demonstrate the DF performance of the triangular antenna array without any angle ambiguities within the field of view from 0◦ to 360◦ in the azimuth direction and from 1.25◦ to 178.75◦ in the elevation direction. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Amplitude modulation

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Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandits 38

Direct Preference-Based Evolutionary Multi-Objective Optimiz...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Huang, Tian Wang, Shengbo Li, Ke School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science University of Exeter United Kingdom

The ultimate goal of multi-objective optimization (MO) is to assist human decision-makers (DMs) in identifying solutions of interest (SOI) that optimally reconcile multiple objectives according to their preferences. Preference-based evolutionary MO (PBEMO) has emerged as a promising framework that progressively approximates SOI by involving human in the optimization-cum-decision-making process. Yet, current PBEMO approaches are prone to be inefficient and misaligned with the DM's true aspirations, especially when inadvertently exploiting mis-calibrated reward models. This is further exacerbated when considering the stochastic nature of human feedback. This paper proposes a novel framework that navigates MO to SOI by directly leveraging human feedback without being restricted by a predefined reward model nor cumbersome model selection. Specifically, we developed a clustering-based stochastic dueling bandits algorithm that strategically scales well to high-dimensional dueling bandits. The learned preferences are then transformed into a unified probabilistic format that can be readily adapted to prevalent EMO algorithms. This also leads to a principled termination criterion that strategically manages human cognitive loads and computational budget. Experiments on 48 benchmark test problems, including the RNA inverse design and protein structure prediction, fully demonstrate the effectiveness of our proposed approach. © 2024 Neural information processing systems foundation. All rights reserved.

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Multi-granularity feature enhancement network for maritime ship detection

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 649-664页

作者： Li Ying Duoqian Miao Zhifei Zhang Hongyun Zhang Witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Project Management Office of China National Scientific Seafloor Observatory Tongji UniversityShanghaiChina Department of Electrical and Computer Engineering AlbertaEdmontonCanada System Research Institute Polish Academy of SciencesWarsawPoland

Due to the characteristics of high resolution and rich texture information,visible light images are widely used for maritime ship ***,these images are suscep-tible to sea fog and ships of different sizes,which can result in missed detections and false alarms,ultimately resulting in lower detection *** address these issues,a novel multi-granularity feature enhancement network,MFENet,which includes a three-way dehazing module(3WDM)and a multi-granularity feature enhancement module(MFEM)is *** 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three-way decisions and FFA-Net to obtain clear image ***,the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super-resolution reconstruction con-volutional neural network to enhance the resolution and semantic representation capa-bility of the feature maps from *** results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Pre-cision metric on two benchmark datasets,achieving 96.28%on the McShips dataset and 97.71%on the SeaShips dataset.

关键词： object classification object recognition rough sets rough set theory

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