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International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Shafaq Rasheed Amal Mirza Muhammad Saad Saeed Muhammad Haroon Yousaf Department of Computer Engineering University of Engineering and Technology Taxila Pakistan Swarm Robotics Lab National Center of Robotics and Automation Taxila Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Waste disposal into water bodies is a serious concern for environmental engineers, often resulting in urban flooding, soil degradation in agricultural areas, and freshwater pollution. Additionally, trash accumulation in water bodies threatens ma-rine life by introducing contaminants through storm drains, rivers, and other channels. Furthermore, the unique character-istics of underwater environments, including high noise levels, low visibility, and color variation, pose challenges for trash detection. Thus, addressing this issue is crucial for environmental sustainability. The work focuses on both floating and submerged trash in water bodies. Typically, the detection of trash on water bodies is impacted by environmental variability, diverse appearance, and background clutter. These conditions also make usual object detection techniques less effective. Deep learning methods have shown promise in handling challenging visual tasks, therefore researchers have turned to them to get over these challenges. In this work, we focus on applying different versions of YOLO to the problem of water trash detection. To assess these models' performance, we use four different datasets: Trash Flow rate Dataset, DeepTrash, FloW, and TrashICRA. Each dataset represents various trash types under different conditions such as underwater trash and floating trash. Our study attempts to identify the best method suitable for the detection of aquatic waste. Our results highlight the effectiveness of YOLO variants in identifying the presence of trash in water ecosystems. By leveraging Grad-CAM we gain insights into the interpretability and transparency of deep learning models thus enabling Explainable AI. This approach will also contribute to the understanding and trustworthiness of artificial systems deployed for trash detection applications by providing a transparent view of decision making process of deep learning models.

关键词： Deep learning YOLO Visualization Explainable AI Storms Biological system modeling Soil Water pollution Waste disposal Robotics and automation

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Transient events during photic driving in single-trial EEG within the second harmonics

Transient events during photic driving in single-trial EEG w...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Hannes Oppermann Jens Haueisen Department of Computer Science and Automation Insititute of Biomedical Engineering and Informatics Technische Universität Ilmenau Ilmenau Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Intermittent photic stimulation (IPS) is a commonly used activation method in clinical applications, e.g. epilepsy, but also used in research for investigating excitability states in the brain. Effects in the brain followed by IPS are also known as photic driving response. These effects are usually characterized using averaged data. Here, we used single-trial analysis to determine photic driving effects at the second harmonics of the stimulation *** rest and auditory-visual stimulation, 64-channel EEG data were recorded with eyes closed. The visual stimulation included eight stimulation frequencies within the alpha and beta bands. A complex Morlet wavelet was used for time-frequency (TF) analysis. Bursts within the TF representations were then detected and characterized by number, frequency, and power near the second *** observed bursts close to the second harmonic of each stimulation frequency. The number of these events increased during stimulation and the main frequencies were shifted towards the second harmonics. Similarly, the power increased at all but one *** new single-trial analysis during IPS could lead to a better understanding of the photic driving effects in the brain and, in particular, the generation mechanisms with regard to the occurrence of effects in the harmonics and sub-harmonics.

关键词： Visualization Time-frequency analysis Epilepsy Power system harmonics Harmonic analysis Wavelet analysis Electroencephalography IP networks Transient analysis engineering in medicine and biology

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Backpropagation Through Agents

arXiv

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arXiv 2024年

作者： Li, Zhiyuan Zhao, Wenshuai Wu, Lijun Pajarinen, Joni School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Electrical Engineering and Automation Aalto University Finland

A fundamental challenge in multi-agent reinforcement learning (MARL) is to learn the joint policy in an extremely large search space, which grows exponentially with the number of agents. Moreover, fully decentralized policy factorization significantly restricts the search space, which may lead to sub-optimal policies. In contrast, the auto-regressive joint policy can represent a much richer class of joint policies by factorizing the joint policy into the product of a series of conditional individual policies. While such factorization introduces the action dependency among agents explicitly in sequential execution, it does not take full advantage of the dependency during learning. In particular, the subsequent agents do not give the preceding agents feedback about their decisions. In this paper, we propose a new framework Back-Propagation Through Agents (BPTA) that directly accounts for both agents’ own policy updates and the learning of their dependent counterparts. This is achieved by propagating the feedback through action chains. With the proposed framework, our Bidirectional Proximal Policy Optimisation (BPPO) outperforms the state-of-the-art methods. Extensive experiments on matrix games, StarCraftII v2, Multiagent MuJoCo, and Google Research Football demonstrate the effectiveness of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Backpropagation

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Analysis of Magnetic Field Simulations of Magnetoelastic Sensors Quasi-Statically using COMSOL Multiphysics and MATLAB Software 5

Analysis of Magnetic Field Simulations of Magnetoelastic Sen...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Gans, Simon Molnar, Jan Naida, Vitalii Maliakova, Mariia Technical University of Košice Department of Theoretical and Industrial Electrical Engineering Košice Slovakia Technical University of Košice Department of Theoretical and Electrical Engineering Košice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Department of Automation and Computer Systems Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Electrical Engineering Kremenchuk Ukraine

ISBN: (纸本)9798350359787

This work describes a procedure in which a transient simulation of electromagnetic fields can be done when the electrical conductivity of the ferromagnetic medium can be effectively neglected transforming it into a quasi-static problem. A pressductor-type magnetoelastic sensor simulation model was created. The sensor's output consists of a change of the induced voltage in the secondary coil due to applied compressive stress. Induced voltage is proportional to the time-derivative of the magnetic flux flowing through the coil and therefore is transient in nature. However, transient analyses tend to be computationally expensive and can yield noisy outputs when the model is not sufficiently densely discretized and appropriate tolerances are chosen. To overcome this, a quasi-stationary analysis of the same system is introduced and computed using the COMSOL Multiphysics FEM simulation software with further processing in the MATLAB environment, and the results are compared. © 2023 IEEE.

关键词： Multiphysics

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Just: towards jute pest classification by combination of supervised learning and triplet loss aided contrastive learning

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Iran Journal of computer Science 2025年第2期8卷 365-378页

作者： Kundu, Shreyan Mukhopadhyay, Souradeep Talukdar, Rahul Das, Semanti Adhikari, Subhajit Computer Science and Engineering (Artificial Intelligence) Institute of Engineering and Management Iem Management Building D-1 Street No. 13 EP Block Sector V Bidhannagar West Bengal Kolkata 700091 India Computer Science and Automation Indian Institute of Science CV Raman Road Karnataka Bangalore 560012 India Computer Science and Engineering (core) Institute of Engineering and Management Iem Management Building D-1 Street No. 13 EP Block Sector V Bidhannagar West Bengal Kolkata 700091 India Department of Lifescience Christ University Hosur Main Road Bhavani Nagar Karnataka Bangalore 560029 India Department of Information Technology RCCIIT KOLKATA West Bengal Kolkata 700015 India

Jute is a vital agricultural commodity contributing significantly to the GDP of countries like Bangladesh, India, Myanmar, and China. However, because of its inaccuracy and slowness, its vulnerability to pest infestations-which are often handled by manual inspections-poses serious cost concerns. This study suggests a unique method for early and accurate pest identification that combines contrastive and supervised learning. Contrastive learning enhances feature representation by distinguishing between positive and negative samples, ensuring that instances within the same class are closely grouped while maintaining separation between different classes. It reduces false negatives by classifying some samples as negative and others with the same label as positive. Supervised learning enables precise pest identification by aligning features with distinctive characteristics of each class. Metrics including precision, recall, F1 score, ROC curve, and confusion matrix are used to assess the hybrid model’s performance;the findings show notable accuracy gains over conventional techniques. This scalable and dependable solution lowers losses caused by pests and provides a sustainable method of growing jute using cutting-edge advanced machine-learning techniques. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Augmentation Contrastive learning Supervised learning Triplet loss

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A Novel High Step-up Switched Capacitor DC-DC Converter Based on Coupled Inductors 4

A Novel High Step-up Switched Capacitor DC-DC Converter Base...

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4th International Conference on Power and Energy Technology, ICPET 2022

作者： Bai, Xuechen Liu, Hao Gu, Jingyun Cai, Hongye Guan, Yueshi Xu, Dianguo University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada Harbin Institute of Technology School of Electrical Engineer and Automation Harbin China

ISBN: (数字)9781665480796

ISBN: (纸本)9781665480796

A novel high step-up switched capacitor DC-DC converter based on coupled inductors is proposed in this paper. The proposed topology solves the problem of insufficient voltage on the switched capacitor, thus improves the traditional energy storage structure. Combined with the coupled inductor, which further improve the voltage gain characteristics and reduce the stress of the switching device. The operating principle and steady state analysis of the converter are introduced in details, and a 200W experimental prototype with an input voltage of 25-45V and an output voltage of 380V is established in the laboratory to verify the feasibility of the converter. © 2022 IEEE.

关键词： Gain measurement

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Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud

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International Journal of Intelligent Networks 2023年第1期4卷 301-309页

作者： Mageshkumar, Nagappan Swapna, J. Pandiaraj, A. Rajakumar, R. Krichen, Moez Ravi, Vinayakumar Department of Computer Science and Technology Madanapalle Institute of Technology and Science Madanapalle India Department of Computer Science & Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur chennai India Al-Baha University Alaqiq 65779-7738 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar34754 Saudi Arabia Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India

Data deduplication is a crucial technique in the field of data compression that aims to eliminate redundant copies of recurring data. This technique has gained significant popularity in the realm of cloud storage due to its ability to effectively reduce storage requirements and optimize bandwidth utilization. To ensure the safeguarding of sensitive data while simultaneously facilitating deduplication, researchers have put forth the concept of convergent encryption as a potential solution. This technique involves encrypting the data prior to its outsourcing, thereby enhancing the confidentiality of the information. In this work, an earnest endeavor is undertaken to formally tackle the issue of authorized data deduplication, with the aim of enhancing data security. Our approach combines the Diffie-Hellman algorithm and symmetrical external decision to protect and popularize information, ensuring end-to-end encryption to encourage user adoption of cloud storage. The proposed model employs block-level deduplication and guarantees the randomness of ciphertexts by generating encryption keys using the Diffie-Hellman algorithm. This method effectively counters both internal and external brute-force attacks, enhancing data security while reducing computational costs. An extensive experimentation is carried out to demonstrate that our approach is particularly beneficial in scenarios with multiple privilege sets. Overall, the proposed model offers an elaborate framework that maintains data privacy and strengthens security measures, contributing to a more efficient and secure cloud-based document search. © 2023 The Author(s)

关键词： Cryptography

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CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications

arXiv

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arXiv 2024年

作者： Zhang, Tianfang Li, Lei Zhou, Yang Liu, Wentao Qian, Chen Hwang, Jenq-Neng Ji, Xiangyang SenseTime Research The Department of Automation Tsinghua University Beijing100190 China Department of Electrical and Computer Engineering University of Washington United States Department of Computer Science University of Copenhagen Denmark SenseTime Research Department of Automation Tsinghua University Beijing100190 China

Vision Transformers (ViTs) mark a revolutionary advance in neural networks with their token mixer’s powerful global context capability. However, the pairwise token affinity and complex matrix operations limit its deployment on resource-constrained scenarios and real-time applications, such as mobile devices, although considerable efforts have been made in previous works. In this paper, we introduce CAS-ViT: Convolutional Additive Self-attention Vision Transformers, to achieve a balance between efficiency and performance in mobile applications. Firstly, we argue that the capability of token mixers to obtain global contextual information hinges on multiple information interactions, such as spatial and channel domains. Subsequently, we propose Convolutional Additive Token Mixer (CATM) employing underlying spatial and channel attention as novel interaction forms. This module eliminates troublesome complex operations such as matrix multiplication and Softmax. We introduce Convolutional Additive Self-attention(CAS) block hybrid architecture and utilize CATM for each block. And further, we build a family of lightweight networks, which can be easily extended to various downstream tasks. Finally, we evaluate CAS-ViT across a variety of vision tasks, including image classification, object detection, instance segmentation, and semantic segmentation. Our M and T model achieves 83.0%/84.1% top-1 with only 12M/21M parameters on ImageNet-1K. Meanwhile, throughput evaluations on GPUs, ONNX, and iPhones also demonstrate superior results compared to other state-of-the-art backbones. Extensive experiments demonstrate that our approach achieves a better balance of performance, efficient inference and easy-to-deploy. Our code and model are available at: https://***/Tianfang-Zhang/CAS-ViT © 2024, CC BY.

关键词： Semantic Segmentation

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Discharge image reconstruction and frequency domain analysis based on event data

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High Voltage 2024年第6期9卷 1195-1201页

作者： Quan Yuan Lei Deng Hao Guo Qishen Lyu Xin Zhang Jibin Wu Yu Deng Hongyang Zhou Xilin Wang Zhidong Jia Engineering Laboratory of Power Equipment Reliability in Complicated Coastal Environments Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhenGuangdongChina Department of Precision Instrument Center for Brain Inspired Computing ResearchTsinghua UniversityBeijingChina College of Information and Computer Taiyuan University of TechnologyTaiyuanChina Shenzhen Power Supply Corporation China Southern Power Grid CorporationShenzhenChina Department of Computing The Hong Kong Polytechnic UniversityHong KongChina China Electric Power Research Institute BeijingChina School of Electrical Engineering and Automation Xiamen University of TechnologyXiamenChina

Optical image method has been the earliest and most used direct method for observing gas ***,research on gas discharge monitoring based on visible light mainly relies on high-speed cameras,but the large size,significant data storage requirements,and susceptibility to interference from complex backgrounds and lighting conditions limit their further *** vision sensing(DVS)technology is a neuromorphic sensing technology that asynchronously measures the luminance changes at each *** offers advantages such as a large dynamic range(>120 dB),high temporal resolution(up to 1µs),and small data volume(MB level).In this study,dynamic vision sensing technology was employed to monitor both 30 mm short-gaps and 1080 mm long-gaps discharge processes *** study developed the CountImage encoding method for event data and conducted image reconstruction,time-domain analysis,and frequency-domain charac-teristic analysis based on the event *** results show that the event-reconstructed images are highly consistent with the high-speed camera images,and the arc develop-ment process and its path can also be clearly ***,this study discovered a correlation between the electrical characteristics and event information during the discharge *** the time domain,the duration of the maximum DVS event count closely matches the duration during which the voltage drops to zero during *** the frequency domain,the Pearson correlation coefficient between the event stream spectrum and the voltage signal spectrum is greater than *** the maximum number of brightening events(ONmax)and the maximum number of darkening events(OFFmax)are positively correlated with the voltage applied between the *** study demonstrates that,compared to the GB/s data rate of high-speed cameras,this approach can record the discharge process and accurately reconstruct the discharge process,arc morphology,and discharge path at M

关键词： image analysis holds

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Exploiting hybrid features extracted from color spaces of dermoscopic images 28

Exploiting hybrid features extracted from color spaces of de...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Moldovanu, Simona Miron, Mihaela Culea-Florescu, Anisia Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Computer Science and Information Technology 47 Domneasca Street 800008 Galaţi800008 Romania Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Electronics and Telecommunications 47 Domneasca Street 800008 Galaţi800008 Romania

ISBN: (纸本)9798350339918

In the context of detecting dangerous illnesses of the patient affected by skin lesions, this study is focused on the problem of improving the classification of melanoma vs. normal nevus considering neural network and new proposed features based on high and low pixels intensity, and densities of the pattern detected with hit-or-miss (HoM) morphological transform. A large set of features from color spaces such as RGB (red, green, and blue), HSV (hue, saturation, and value) and L∗a∗b∗ (lightness variable L∗, chromaticity indices a∗ and b∗) were initially extracted and grouped in two parts: the first group includes first and second order features, and the second group contains hybrid features (a mix of first order, second order features and proposed features). The dimensionality of the features from these two groups is considerably reduced with principal component analysis (PCA) statistical method and fed three different types of neural network (NN): Radial Basis Function Neural Network (RBFNN), Cascade-Forward Neural Network (CFNN) and Pattern Recognition Neural Network (PRNN). From this corroboration of hybrid features, color space and NN, the L∗a∗b∗ color space with CFNN provided accuracy (ACC) of 0.897, F1-score of 0.91 and area under the ROC curve (AUC) of 0.896, respectively. © 2023 IEEE.

关键词： Color

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