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Topology Construction for Max-Min Rate Optimization in Heterogeneous UAVs Networks

IEEE Internet of Things Journal 2025年第12期12卷 20203-20214页

作者： Wu, Kefeng Chin, Kwan-Wu Soh, Sieteng Sun Yat-sen University School of Cyber Science and Technology Guangzhou510275 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Curtin University School of Electrical Engineering Computing and Mathematical Sciences PerthWA6102 Australia

This paper considers a topology construction problem involving a fixed-wing Unmanned Aerial Vehicle (UAV), a set of quad-rotor UAVs and mobile ground users. The constructed topology must maximize the max-min flow rate of ground users over a given planning horizon. To this end, we outline a Mixed Integer Linear Program (MILP) that jointly optimizes the trajectory of the fixed-wing UAV, placement of quad-rotor UAVs, and routing of traffic from each source-destination ground user pair. Solving the MILP is challenging because it requires an exhaustive collection of topologies. To this end, this paper outlines a solution called Rollout to determine the network topology in each time slot of a given planning horizon iteratively. The main idea of Rollout is to generate a sequence of future decisions using a heuristic, where a decision corresponds to the placement of quad-rotor UAVs. Further, each sequence of decisions has a cost-to-go value. Rollout then selects the sequence with the highest cost-to-go value. The results show that the max-min flow rate achieved by Rollout is on average 81% that of MILP. © 2014 IEEE.

关键词： Antenna grounds

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Comparative analysis of machine learning and ensemble approaches for hepatitis B prediction using data mining with synthetic minority oversampling technique

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Health and technology 2024年第1期14卷 109-118页

作者： Alizargar, Azadeh Chang, Yang-Lang Tan, Tan-Hsu Liu, Tsung-Yu Department of Electrical Engineering College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei 10608 Taiwan Department of Multimedia and Game Science Lunghwa University of Science and Technology Taoyuan 333326 Taiwan

Purpose: Hepatitis B, caused by the Hepatitis B virus (HBV), can harm the liver without noticeable symptoms. Early detection is crucial to prevent transmission and enhance recovery. The main goal is to predict Hepatitis B through cost-effective lab test data, by utilizing machine learning. The primary focus is on evaluating the effectiveness of various algorithms in predicting the disease and their potential to enhance early diagnosis capabilities. Methods: Six distinct algorithms (Support Vector Machine, K-nearest Neighbors, Logistic Regression, decision tree, extreme gradient boosting, random forest) were employed alongside an ensemble model. Analysis involved two rounds: considering all features and key attributes. The Synthetic Minority Oversampling Technique (SMOTE) was employed for data imbalance. Various metrics, including the confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC) curve, area under the curve (AUC), and mean absolute error (MAE), were utilized to assess the efficacy of each predictive technique. The National Health and Nutrition Examination Survey (NHANES) dataset was employed. Results: The experimental results demonstrate that the ensemble model attained the highest accuracy (97%) and AUC (0.997) in comparison to existing models. The analysis revealed that specific crucial features possess substantial predictive significance within this model. Conclusion: The study underscores the potential of the ensemble model as a valuable tool for medical practitioners, leveraging cost-effective and readily obtainable laboratory test data to predict Hepatitis B with remarkable accuracy. By facilitating early diagnosis and intervention, this research presents a promising avenue to enhance patient outcomes in the context of Hepatitis B. © 2023, The Author(s) under exclusive licence to International Union for Physical and engineering Sciences in Medicine (IUPESM).

关键词： Early detection Ensemble model Index terms— Hepatitis B Liver damage Machine learning SMOTE

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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Spectral Estimation of Weak Signals with Strong Interference via Modulo Sampling 59

Spectral Estimation of Weak Signals with Strong Interference...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Ahmed, Shoaib Zeng, Cengcang Li, Hongbin Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

ISBN: (纸本)9798331513269

This paper introduces a framework for spectral analysis of weak signals in the presence of strong interference that can potentially exceed the dynamic range of the analog-to-digital converter (ADC) employed for data acquisition. The framework combines modulo sampling to avoid ADC saturation with interference cancellation (IC) and adaptive spectral estimators such as amplitude and phase estimation of a sinusoid (APES) and Capon for high-resolution spectral analysis. Numerical simulations validate the proposed approach's ability to accurately recover weak signals' spectrum with significantly lower mean-squared error (MSE) in amplitude estimation compared to conventional methods in the considered scenarios. Notably, it is shown that APES and Capon are able to effectively reject the out-of-range interference and reveal weak spectral content, without requiring the interference cancellation step. © 2025 IEEE.

关键词： Mean square error

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Voltage Profile Improvement and Loss Minimization in a Transmission Line Using FLC-Based UPFC at Bahir Dar Substation-II 230 kV Line 11th

Voltage Profile Improvement and Loss Minimization in a Trans...

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11th EAI International Conference on Advancement of Science and technology, ICAST 2023

作者： Birlie, Minale School of Electrical and Computer Engineering Dire Dawa University Institute of Technology Dire Dawa Ethiopia

ISBN: (纸本)9783031641503

This article examines the power flow and voltage profile of a transmission line system. A flexible alternating current transmission system (FACTS) device named unified power flow controller (UPFC) incorporating fuzzy logic controller (FLC) has been implemented to reduce power loss and enhance voltage profile after the current network analysis was completed. Utilizing Newton-Raphson load flow analysis, the weakest bus in a system is identified for the installation of UPFC. The 341-km-long, 230 kV line that runs from Bahir Dar to Alamata serves as the validation point for this controller’s operation. Therefore, to demonstrate the performance of FLC-UPFC, a comparison has been done using conventional proportional-integral (PI)-based UPFC, FLC-UPFC, and without UPFC at the same load bus. For example, receiving end voltage improves by 11% when PI-UPFC is used, while real and reactive power flows improve by 13.7% and 8.6%, respectively. Voltage rises by 14.9% when the recommended FLC-UPFC is used, while the real and reactive power flows have improved by 21.6% and 15.05%, respectively. The findings indicate that the FLC-UPFC technique, which is being proposed, performs better than the conventional PI controller. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Load flow control

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A Comparative Study of Heart Sound Denoising Using Adaptive Line Enhancer with LMF Algorithm 12

A Comparative Study of Heart Sound Denoising Using Adaptive ...

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12th International electrical engineering Congress, iEECON 2024

作者： Pongyart, Witthawas Ongcunaruck, Wisute Faculty Of Engineering King Mongkut's University Of Technology North Bangkok Electrical And Computer Engineering Department Bangkok Thailand

ISBN: (纸本)9798350383591

This paper presents a comparative study of heart sound denoising techniques by employing the Adaptive Line Enhancer (ALE) with the Least Mean Fourth (LMF) algorithm. Three variations of the ALE using LMF are developed and evaluated. The first approach replaces the conventional Least Mean Square (LMS) algorithm with LMF in the ALE structure. The second approach adopts a transform domain framework, and a Hadamard domain LMF is developed and used for ALE. Since the transform domain LMF requires the FIR filter order to match the dimension of the transformation, a novel structure of the transform domain ALE is developed, allowing the FIR filter order to be independent of the transform dimension. The study evaluates the performance of the ALE with LMF algorithm in both domains, alongside the proposed new structure. Simulated tests, focusing on steady-state behavior as the key performance index, demonstrate the superior performance of the newly developed transform domain ALE, surpassing both the time domain ALE with LMS and LMF algorithm and the conventional transform domain approach. © 2024 IEEE.

关键词： Time domain analysis

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Reinforcement Learning Enhanced Controller Synthesis for Autonomous Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-12页

作者： Dong, Yanqi Dong, Wei Yang, Dong Tang, Ying Xu, Rongjia College of Computer Science National University of Defense Technology Changsha China Department of Electrical and Computer Engineering Rowan University Glassboro NJ USA

Developing control programs for autonomous vehicles is a challenging task, mainly due to factors such as complex and dynamic environments, intricacy of tasks, and uncertain sensor information. To tackle the challenge, this paper harnesses the potential of formal methods and deep reinforcement learning (DRL) for a more comprehensive solution that integrates Generalized Reactivity(1) (GR(1)) synthesis with DRL. The GR(1) synthesis module takes care of high-level task planning, ensuring a vehicle follows a correct-by-construction and verifiable plan for its mission. On the other hand, the DRL model operates as the low-level motion controller, allowing the vehicle to learn from experience and adjust its actions based on real-time sensor feedback. Therefore, the resulting controller for autonomous vehicles is not only guaranteed to finish its designated tasks but also intelligent to handle complex environments. Through comparative experimental studies, we demonstrate that the control program generated by the proposed approach outperforms the ones generated independently utilizing GR(1) reactive synthesis and DRL. IEEE

关键词： Reinforcement learning

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Renewable Sources - A Genetic Perspective and Application Architecture for Improved Underwater Astronomy Observations 15

Renewable Sources - A Genetic Perspective and Application Ar...

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15th International Conference Elektro, ELEKTRO 2024

作者： Periola, A.A. Adonis, M. Cape Peninsula University of Technology Department of Electrical Electronic and Computer Engineering South Africa

ISBN: (纸本)9798350372359

The conduct of underwater astronomy is an important scientific investigation and procedure. Underwater astronomy observations require the provisioning of power to operate underwater telescopes. The observation should be done while enhancing the angular resolution and not constituting an extra load on the grid. Underwater astronomy uses underwater telescopes that are needed to ubiquitously detect neutrinos. The research proposes a novel light interception approach for the generation of electricity in the deep underwater environment. The proposed energy utilizes bio-engineered bioluminescence to realize electrical energy for underwater telescope operation. The use of the proposed energy source enables underwater telescopes to be located at different underwater locations with significant capacity to enhance the baseline. The use of the proposed approach for powering underwater telescopes enhances the observation i.e., angular resolution by an average of (34.8 - 47.8)%. © 2024 IEEE.

关键词： Electric loads

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Braille–Latin conversion using memristive bidirectional associative memory neural network

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12511-12534页

作者： Vaidyaraman, Jayasri Thyagarajan, Abitha K. Shruthi, S. Ravi, V. School of Electronics Engineering Vellore Institute of Technology Chennai India Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

Artificial neural networks (ANNs) are finding increasing use as tools to model and solve problems in almost every discipline in today’s world. The successful implementation of ANNs in software—particularly in the fields of deep learning and machine learning—has spiked an interest in designing hardware architectures that are custom-made to implement ANNs. Several categories of ANNs exist. The two-layer bidirectional associative memory (BAM) is a particular class of hetero-associative memory networks that is extremely efficient and exhibits good performance for storing and retrieving pattern pairs. The memristor is a novel hardware element that is well-suited to modelling neural synapses because it exhibits tunable resistance. In this work, in order to create a device that can perform Braille–Latin conversion, we have implemented a circuit realization of a BAM neural network. The implemented hardware BAM uses a memristor crossbar array for modelling neural synapses and a neuron circuit comprising an I-to-V converter (resistor), voltage comparator, D flip-flop, and inverter. The efficiency of the implemented hardware BAM was tested initially using 2 × 2 and 3 × 3 patterns. Upon successfully verifying the ability of the implemented BAM to store and retrieve simple pattern pairs, it was trained for a pattern-recognition application, namely mapping Braille alphabets to their Latin counterparts and vice versa. The performance of the implemented BAM network is robust even on the introduction of noise. The application can recognize the input patterns with accuracies of 100% in either direction when tested with up to 30% noise. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Neural networks

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Machine Learning Based Pattern Recognition with HOG Features for Detection of Islanding 3

Machine Learning Based Pattern Recognition with HOG Features...

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3rd IEEE International Conference on electrical Power and Energy Systems, ICEPES 2024

作者： Pal, Kumaresh Goswami, Subhadip Bhuyan, Anshuman Koley, Santanu Bera, Bidisha Basu, Sarmistha National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Haldia Institute of Technology Department of Computer Science Engineering Haldia India Global Institute of Science and Technology Department of Computer Science and Technology Haldia India Calcutta Institute of Engineering and Management Department of Physics Kolkata India

ISBN: (纸本)9798350390728

Islanding is a significant difficulty that arises from the high integration of distributed generation sources. Islanding could potentially damage the clients and their equipment. According to the IEEE 1547 DG interconnection requirements, islanding must be detected within two seconds, and the DG must be shut down. This research introduces a new method for identifying islanding using image pattern recognition utilising a Machine Learning classification algorithm. The suggested technique utilises the Random Forest (RF) classification algorithm. Images are utilised to extract the histogram of oriented gradient (HOG) features in order to detect both non-islanding and islanding occurrences. The spectrogram images are formed from the time-series signal collected from the point of common coupling. The images are utilised to extract features from histogram of oriented gradient, which are subsequently used as feature vector inputs for training and testing the RF classifiers. The spectrogram image is derived using the rate of change of the voltage parameter. The efficacy of the RF classifier is evaluated using 5, 10, 20, and 25-fold cross-validations. The classification findings indicates that islanding detection utilising a Random Forest classifier and Histogram of Oriented Gradients (HOG) feature for image pattern recognition obtained outstanding performance with 98.75% accuracy and a detection time of 192 milliseconds. © 2024 IEEE.

关键词： Spectrographs

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