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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Swetha, S. Babu, Nirmal Varghese Karunya Institute of Technology and Science Division of Computer Science and Engineering Karunya Nagar Tamil Nadu Coimbatore India Karunya Institute of Technology and Science Division of Data Science and Cyber Security Karunya Nagar Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

Emotions, the essence of human connection and understanding, take center stage in this pioneering research on emotion recognition. We look at identifying common emotions including joy, sorrow, neutral, anger, fear, and surprise. Introducing an innovative approach, we seamlessly blend advanced computer vision algorithms and intricate signal processing techniques to concurrently analyze facial expressions and EEG signals. The heart of our methodology lies in the utilization of the state-of- the-art Deep Face Analyzer, guided by the deep2face algorithm for meticulous interpretation of facial features, coupled with a spectrographic analyzer translating EEG signals into spectrograms. The overarching goal is to enhance the accuracy and resilience of emotion recognition, with the added dimension of a real-time Waveform Extractor for dynamic evaluation of audio signals. Within the landscape of diverse algorithms and modalities, the Hybrid Model (CNN + LSTM) emerges as a standout performer, achieving an impressive 88.2% accuracy by synergizing facial expressions and EEG signals. The strategic integration of long short-term memory (LSTM) networks with convolutional neural networks (CNNs) underscores the model's exceptional capacity to capture the nuanced intricacies of human emotions. Beyond mere technological advancement, this study underscores the importance of embracing multiple modalities for a holistic understanding of emotions, positioning the Hybrid Model as a powerful and nuanced tool for precise and multiclass emotion identification across diverse real-world scenarios. In essence, our research charts a path where the realm of emotions converges with technological innovation, fostering a deeper understanding of the human experience © 2024 IEEE.

关键词： Emotion Recognition

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A 12-Slot 10-Pole Induction Motor with Wave Winding Rotor 11

A 12-Slot 10-Pole Induction Motor with Wave Winding Rotor

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11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Yokoi, Y. Nagasaki University Division of Electrical Engineering and Computer Science Japan Nagasaki University Organization for Marine Science and Technology Japan

ISBN: (纸本)9788957083505

In this paper, we investigate the effect of rotor winding configurations on driving an induction motor (IM) with a 12-slot 10-pole concentrated winding configuration of the stator, which is one of the fractional slot concentrated winding (FSCW) configurations. For the stator FSCW configuration, conventional rotor squirrel-cage windings are inadequate for sufficient drive torque. To address the issue, we employ rotor wave windings for the stator FSCW configuration to trap driving harmonic and completely eliminate the effects of other dominant harmonics in the stator rotating magnetic field (RMF). The theoretical and numerical analyses confirm the effectiveness of the rotor wave windings in filtering out undesirable stator RMF harmonics and improving the performance of the IM. © 2023 The Korean Institute of Power Electronics.

关键词： Stators

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Biotic Stress Management in Soil-Less Agriculture Systems: A Deep Learning Approach for Identification of Leaf Miner Pest Infestation 5

Biotic Stress Management in Soil-Less Agriculture Systems: A...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Subeesh, A. Chauhan, Naveen Department of Computer Science and Engineering National Institute of Technology HP Hamirpur177005 India Agricultural Mechanization Division ICAR - Central Institute of Agricultural Engineering MP Bhopal462038 India

Leaf miner pests pose a serious threat to the productivity, profitability, and sustainability of soil-less tomato cultivation systems. Early and accurate identification of leaf miner infestation is crucial for timely pest control measures. This study presents an efficient approach using attention-based convolutional neural networks for timely identification of this pest infestation. The proposed approach uses both spatial and channel attention modules to enhance the feature extraction capability of the convolutional neural network. The custom model developed was trained using an image dataset collected from tomatoes grown in a hydroponic environment. The different hyper parameters were tuned to get the optimal model performance. The experimental results show that the proposed attention-based CNN model achieved an overall accuracy of 97.87%, 97.10% precision, 98.53% recall, and 97.81% F1-score. Additionally, the model performance was compared with other pre-trained models viz., AlexNet, VGG16, and VGG19, and was found to outperform these state-of-the-art CNN models due to its improved feature extraction capability. The efficiency of the model underlines its potential to be deployed as part of automated pest monitoring systems in hydroponic environments. This work contributes to the development of computer vision and deep learning-based solutions for precision agriculture applications. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films

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光：科学与应用（英文版） 2025年第2期14卷 411-421页

作者： Waqas W.Ahmed Haicheng Cao Changqing Xu Mohamed Farhat Muhammad Amin Xiaohang Li Xiangliang Zhang Ying Wu Division of Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia College of Engineering Taibah UniversityMadinah 42353Saudi Arabia Division of Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Division of Physical Science and Engineering King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Division of Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Department of Computer Science and Engineering University of Notre DameNotre DameIN 46556USA

We propose and demonstrate a data-driven plasmonic metascreen that efficiently absorbs incident light over a wide spectral range in an ultra-thin silicon *** embedding a double-nanoring silver array within a 20 nm ultrathin amorphous silicon(a-Si)layer,we achieve a significant enhancement of light *** enhancement arises from the interaction between the resonant cavity modes and localized plasmonic modes,requiring precise tuning of plasmon resonances to match the absorption region of the silicon active *** facilitate the device design and improve light absorption without increasing the thickness of the active layer,we develop a deep learning framework,which learns to map from the absorption spectra to the design *** inverse design strategy helps to tune the absorption for selective spectral *** optimized design surpasses the bare silicon planar device,exhibiting a remarkable enhancement of over 100％.Experimental validation confirms the broadband enhancement of light absorption in the proposed *** proposed metascreen absorber holds great potential for light harvesting applications and may be leveraged to improve the light conversion efficiency of ultra-thin silicon solar cells,photodetectors,and optical filters.

关键词： silicon learning absorption assisted broadband enhanced films machine metascreen plasmonic

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Enhancing Smart Grid Security with XMSS-Based Blockchain technology 1

Enhancing Smart Grid Security with XMSS-Based Blockchain Tec...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Kathrine, G. Jaspher W. Krittikka, P. Johnraja, Immanuel Kirubakaran, Stewart Salaja, S. Arunkumar, K. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Cse Coimbatore India

ISBN: (纸本)9798350359756

Smart grids are electricity network that can help improve the efficiency, reliability, and security of the power grid. Smart grid uses digital and other advanced technologies to monitor and manage the flow of electricity from generation to consumption It is a technological advancement in the management and delivery of electrical power. But the combination of a variety of IoT gadgets and there are serious security problems with the integration of communication networks into the smart grid infrastructure. The Traditional security methods can easily be broken and it will not be sufficient to safeguard against the modern evolving threats. Quantum resistant smart grids Blockchain is an emerging technology that has the potential to completely change how smart grids function. This study investigates the utilization of the extended Merkle Signature Scheme (XMSS) in combination with using blockchain technology to improve the smart grid's security. We talk about the benefits of XMSS and how to incorporate it into a blockchain-based design to safeguard smart grid systems from a range of security risks. © 2023 IEEE.

关键词： Blockchain

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Sense and Jam: ISAC-Aided Robust Physical Layer Security 58

Sense and Jam: ISAC-Aided Robust Physical Layer Security

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Naim Shaikh, Mohd Hamza Celik, Abdulkadir Eltawil, Ahmed M. Nauryzbayev, Galymzhan School of Engineering and Digital Sciences Nazarbayev University Department of Electrical and Computer Engineering Astana010000 Kazakhstan King Abdullah University of Science and Technology Cemse Division Thuwal Saudi Arabia

ISBN: (纸本)9798350354058

This work proposes a physical layer security (PLS) framework leveraging integrated sensing and communication (ISAC) to facilitate secure communication. The framework employs a multi-antenna full-duplex (FD), dual-functional access point (AP) to establish a secure communication zone around the intended user. The AP performs target sensing to detect the desired user and simultaneously employs artificial noise (AN) to jam any undesired users within the zone. In the first time slot, the AP sweeps the beam from a discrete Fourier transform-based codebook to sense the environment via its transmit antenna array, while the receive antenna array captures echoes from targets. If undesired users are detected within the secure communication zone, the downlink (DL) signal is augmented with friendly AN to obstruct their overhearing attempts. For the uplink (UL), the same set of transmit antennas beams AN towards the undesired users, while the receive antennas capture the UL signals from the desired user. By combining optimal beam selection, target sensing, and AN transmission, an FD secure communication link is established between the AP and desired user. Simulation results show that the proposed scheme significantly improves DL and UL secrecy rates compared to conventional methods. © 2024 IEEE.

关键词： Discrete Fourier transforms

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The Effectiveness of CNN-Based Models in Macroscopic Motion Direction Detection 6

The Effectiveness of CNN-Based Models in Macroscopic Motion ...

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Qiu, Zhiyu Chen, Tianqi Hua, Yuxiao Todo, Yuki Graduate School of Natural Science & Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Kanazawa University Faculty of Electrical Information and Communication Engineering Kanazawa Japan

ISBN: (纸本)9798331527426

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, offering significant advancements in image and video recognition, classification, and segmentation tasks. Leveraging hierarchical pattern recognition capabilities, CNNs have demonstrated superior performance over traditional machine learning methods. One particularly critical application of CNNs is in motion direction detection, which is essential for enhancing security in surveillance systems, ensuring safe navigation in autonomous vehicles, and providing valuable insights in sports analytics. In this study, our primary goal was to investigate how different types of input data impact the accuracy of motion direction detection. We applied five distinct preprocessing methods to test the effect of input data on accuracy. Furthermore, we conducted experiments in various object and background scenarios to evaluate adaptability of simple 4-layer CNN, EfficientNetBO and ResNet50 in detecting motion direction. Finally, we tested the noise resistance of these three models. The results indicate that CNNs are capable of extracting the macroscopic motion direction of objects in images. Our study highlights the potential of CNN-based models for efficient and effective motion direction detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Physics-Constrained Adversarial Training for Neural Networks in Stochastic Power Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1121-1131页

作者： Li, Wenting Deka, Deepjyoti Wang, Ren Paternina, Mario R. Arrieta LANL Applied Mathematics and Plasmas Physics Division Los AlamosNM87544 United States Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States National Autonomous University of Mexico Department of Electrical Engineering Mexico City04510 Mexico

The growth in distributed energy resources (DERs) is an important step toward solving the global climate crisis. However, many DERs, such as wind and solar power, are random and intermittent, causing the data in power grids to be perturbed with high uncertainty. Such perturbations degrade the performance of data-driven algorithms introduced in power grids for sensing and control. Existing approaches can strengthen machine learning models against well-designed malicious attacks. However, such physics-agnostic efforts fail to ensure the robustness of natural perturbations in power grids, that occur due to varying loads and changes in control inputs. This article proposes a novel physics-constrained adversarial training method for robustifying neural networks for the crucial problem of locating edge-faults in power grids. Our approach relies on first deriving analytical physical laws that are satisfied by state perturbations in realistic grids and then using the descriptive sets during adversarial training. Compared to state-of-the-art methods, our perturbation-robust neural networks have higher robust accuracy as well as training efficiency. Also, we demonstrate that the proposed approach achieves a tighter upper bound of robust risks than traditional efforts. The numerical experimental results in the IEEE 68-bus benchmark system validate our adversarial training in two scenarios when loads and control inputs vary randomly. The proposed method shows superior performance in accuracy and efficiency for different perturbed datasets. In addition, we testify the influence of different inputs on the robustness. © 2023 IEEE.

关键词： Neural networks

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Performance-Guaranteed Solutions for Multi-Agent Optimal Coverage Problems using Submodularity, Curvature, and Greedy Algorithms 63

Performance-Guaranteed Solutions for Multi-Agent Optimal Cov...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Welikala, Shirantha Cassandras, Christos G. Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States Boston University Division of Systems Engineering and Center for Information and Systems Engineering BrooklineMA02446 United States

ISBN: (纸本)9798350316339

We consider a class of multi-agent optimal coverage problems in which the goal is to determine the optimal placement of a group of agents in a given mission space so that they maximize a coverage objective that represents a blend of individual and collaborative event detection capabilities. This class of problems is extremely challenging due to the non-convex nature of the mission space and of the coverage objective. With this motivation, greedy algorithms are often used as means of getting feasible coverage solutions efficiently. Even though such greedy solutions are suboptimal, the submodularity (diminishing returns) property of the coverage objective can be exploited to provide performance bound guarantees. Moreover, we show that improved performance bound guarantees (beyond the standard (1-1 / e) performance bound) can be established using various curvature measures of the coverage problem. In particular, we provide a brief review of all existing popular applicable curvature measures, including a recent curvature measure that we proposed, and discuss their effectiveness and computational complexity, in the context of optimal coverage problems. We also propose novel computationally efficient techniques to estimate some curvature measures. Finally, we provide several numerical results to support our findings and propose several potential future research directions. © 2024 IEEE.

关键词： Optimal detection

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An Efficient Internet Traffic Classification System Using Deep Learning for IoT

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computers, Materials & Continua 2022年第4期71卷 407-422页

作者： Muhammad Basit Umair Zeshan Iqbal Muhammad Bilal Jamel Nebhen Tarik Adnan Almohamad Raja Majid Mehmood Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Division of Computer and Electronics Systems Engineering Hankuk University of Foreign StudiesYongin-siKorea Electrical-Electronics Engineering Department Faculty of EngineeringKarabük University78050KarabükTurkey Prince Sattam bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Information and Communication Technology Department School of Electrical and Computer EngineeringXiamen University MalaysiaSepang43900Malaysia

Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central *** IoT devices are connected to a network therefore prone to *** management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic *** to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural ***,machine learning-based models incline to misclassify internet traffic due to improper feature *** this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet *** examine the performance of the proposed technique,Moore-dataset is used for training the *** proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet *** experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.

关键词： Deep learning internet traffic classification network traffic management QoS aware application classification

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