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Unsupervised Graph-Generative Network-based PV Condition Monitoring systems

Unsupervised Graph-Generative Network-based PV Condition Mon...

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IEEE General Meeting Power& Energy Society

作者： Sarah Allahmoradi Shahabodin Afrasiabi Xiaodong Liang Mousa Afrasiabi Jamshid Aghaei Chi Yung Chung Dept. Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada Department of Electrical Engineering School of Energy Systems Lappeenranta University of Technology Lappeenranta Finland Department of Electrical and Computer Engineering The Hong Kong Polytechnique University Hong Kong China

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Condition monitoring plays a pivotal role in the photovoltaic (PV) system’s operation. To overcome issues in conventional PV condition monitoring, in this paper, an unsupervised graph-generative network-based PV condition monitoring system is proposed by considering Gaussian/non-Gaussian noises. The designed network has the ability to handle an imbalanced dataset and high dimensional signals, establish the correlation between time-varying signals in PV systems, and learn spatial-temporal features. The proposed system is validated through an experimental dataset by considering seven abnormal conditions, including PV array faults, PV control system faults, and grid faults. The proposed method appears to be effective under Gaussian/non-Gaussian noises with an accuracy higher than 98%. The superiority of the proposed graph-generative network is demonstrated by comparing with well-known deep- and shallow-based PV condition monitoring systems with 5%- 30% improvements in terms of accuracy and reliability.

关键词： Condition monitoring Photovoltaic systems Accuracy Correlation Noise Probabilistic logic Control systems Robustness Time-varying systems Monitoring

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Meaning of the Clusters on Dimensionality Reduction by Word Clustering 12

Meaning of the Clusters on Dimensionality Reduction by Word ...

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12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022

作者： Deguchi, Toshinori Seo, Sinsuke Ishii, Naohiro Dept. of Electrical and Computer Engineering Gifu College Gifu Japan Interdisciplinary Technology Development Gifu College Gifu Japan Information Systems Architecture Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

In text mining, Latent Semantic Analysis (LSA) is the popular method to reduce the dimension of document vectors. Since LSA produces a set of topics by statistical information, the meaning of each topic is not *** proposed a method to reduce the dimension by clustering the words in the documents. This method produces a set of clusters of words instead of topics. Using Word2vec to vectorize the words, the mean vector of the cluster is calculated, which shows the meaning of the *** this paper, we show the dimensionality reduction and the meaning of the generated clusters by word cloud, on document classification problem with a subset of BBC Dataset. © 2022 IEEE.

关键词： Classification (of information)

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural Networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Reinforcement Learning based Routing in an Air-to-Air Ad-hoc Network 41

Deep Reinforcement Learning based Routing in an Air-to-Air A...

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41st IEEE/AIAA Digital Avionics systems Conference, DASC 2022

作者： Wang, Zhe Han, Ruixuan Li, Hongxiang Knoblock, Eric J. Apaza, Rafael D. Gasper, Michael R. University of Louisville Dept. of Electrical and Computer Engineering LouisvilleKY United States Nasa Glenn Research Center Communications and Intelligent Systems Division ClevelandOH United States

ISBN: (数字)9781665486071

ISBN: (纸本)9781665486071

This paper studies the Multiple Sources and Multiple Destinations (MSMD) routing problem in a dynamic Air-to-Air Ad-hoc Network (AAAN). We consider a spectrum-limited scenario where multiple links have to share the same frequency channel so that co-channel interference becomes inevitable. As a result, routing decisions and spectrum access are coupled and must be jointly considered. This paper proposes a deep Q-learning-based algorithm to find an optimal routing and channel selection strategy that minimizes the end-to-end communication delay. Specifically, under the assumption that only local information is available to every node, the Deep Q-Network (DQN) is trained offline to learn the optimal routing and channel selection strategy. After the trained DQN is implemented in every node, multiple relay nodes can simultaneously determine their next-hop relay and channel selections in real-time. Simulation results demonstrate the efficacy of our proposed algorithm. © 2022 IEEE.

关键词： Reinforcement learning

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Constrained clustering using Gaussian processes 28

Constrained clustering using Gaussian processes

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28th European Signal Processing Conference, EUSIPCO 2020

作者： Traganitis, Panagiotis A. Giannakis, Georgios B. Dept. of Electrical and Computer Engineering and Digital Technology Center University of Minnesota United States

ISBN: (纸本)9789082797053

Constrained clustering is an important machine learning, signal processing and data mining tool, for discovering clusters in data, in the presence of additional domain information. The present work introduces a probabilistic scheme for constrained clustering based on the popular Gaussian Process framework. The proposed scheme accommodates pairwise, must- and cannot-link constraints between data, does not require hyperparameter tuning, and enables assessment of the reliability of obtained results. Preliminary results on real data showcase the potential of the proposed approach. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Signal processing

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Localization of Dengue Virus Likelihood Using Image Processing Technique

Localization of Dengue Virus Likelihood Using Image Processi...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Md. Mahbub Hosen Md. Rahatul Islam Faria Tabassum Syeda Thamina Tonni Malik Mohammad Nafiur Noor Shahid Rahman Dept. of Computer Science & Engineering Northern University of Bangladesh Dhaka Bangladesh Dept. of Human Intelligence Systems Kyushu Institute of Technology Wakamatsu Kitakyushu Japan Dept. of Computer Science & Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Human Intelligence Lab Bangladesh Advance Robotics Research Center Dhaka Bangladesh Dept. of Electrical & Electronic Engineering Canadian University of Bangladesh Dhaka Bangladesh

The Aedes mosquito, found in tropical areas, transmits, and causes dengue fever. The spread of the dengue fever virus from an infected Aedes mosquito bite typically takes between three and fifteen days to manifest itself clinically as dengue fever symptoms. Early and automated detection of this illness will reduce the occurrence of outbreaks and facilitate the digitization of the system. Our primary objective is to identify regions at high risk for dengue fever using limited image data through the utilization of sophisticated machine learning and image processing methods. This work showcases the application of transfer learning algorithms for the timely identification of regions at risk for dengue mosquito infestation, particularly in situations where obtaining early data on dengue outbreaks is challenging. The experiments included images of 2150 dengue-positive data sets and 1440 dengue-negative data sets. We used AI and image classifiers Yolo V4 Tin, Tensor Flow, Google Colab, etc. for the training data set, and the system makes predictions with a 99.63% accuracy rate. Our system has some unique attributes; it not only detects the percentage of dengue probability but also the specific dengue-prone location on the submitted image.

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LoRaWAN Scheduling Mechanism for 6G-Based LEO Satellite Communications

LoRaWAN Scheduling Mechanism for 6G-Based LEO Satellite Comm...

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IEEE Conference on Vehicular technology (VTC)

作者： Abhijeet Manoj Varma Nikumani Choudhury Jay Dave Anakhi Hazarika Moustafa M. Nasralla Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Electrical & Electronics Engineering Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Communications & Networks Engineering Smart Systems Engineering Lab Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

As the Internet of Things (IoT) is poised to become a global phenomenon, it is imperative to schedule the transmissions of IoT devices effectively and in a fair way. Leveraging Long Range (LoRa) technology, we can achieve transmissions that consume minimal power while covering vast distances, aligning with the requirements of IoT devices. However, the proximity of multiple devices within the same area often leads to packet interference and collisions. To address this, our study introduces a pioneering scheduling method utilizing a constellation of Low Earth Orbit (LEO) satellites to manage and streamline the transmission of data from End Devices (EDs). This method employs two LEO satellites: the first satellite assigns the sequence for EDs to dispatch their packets, and the second collects these packets in the predetermined sequence before forwarding them to the LoRa Network Server (LNS). For urgent (URG) communications, EDs can alert the first satellite, which then coordinates with the LNS to schedule these priority transmissions. The LNS generates a schedule that is relayed to the second satellite, informing EDs with URG packets of their specific transmission times and channels. This scheduling approach is designed to optimize channel usage effectively while accommodating the transmission of urgent data.

关键词： 6G mobile communication Schedules Vehicular and wireless technologies Satellites Scheduling algorithms Low earth orbit satellites LoRaWAN

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A Decision Making Model Where the Cell Exhibits Maximum Detection Probability: Statistical Signal Detection Theory and Molecular Experimental Data 57

A Decision Making Model Where the Cell Exhibits Maximum Dete...

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57th Annual Conference on Information Sciences and systems, CISS 2023

作者： Emadi, Ali Lipniacki, Tomasz Levchenko, Andre Abdi, Ali New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ United States Institute of Fundamental Technological Research Polish Academy of Sciences Warsaw Poland Yale University Yale Systems Biology Institute Dept. of Biomedical Eng. New HavenCT United States New Jersey Institute of Technology Dept. of Elect. Comput. Eng. Dept. of Biological Sciences NewarkNJ United States

ISBN: (纸本)9781665451819

Molecular noise and signaling abnormalities in biochemical signaling systems in cells affect signaling events and consequently may alter cellular decision making results. Since unexpected and altered cellular decisions may contribute to the development of many pathological conditions and diseases, it is of interest to develop proper models to characterize and measure molecular signal detection parameters and cellular decisions. In this paper and using the Neyman-Pearson signal detection theorem, we propose a signal detection model in which the cell maximizes its signal detection probability in the presence of noise. To evaluate the usefulness of the proposed model, we use measured molecular experimental data of the important TNF-NF-κB cell signaling system. Our results demonstrate that the proposed model provides biologically relevant findings. The introduced Neyman-Pearson-based molecular signal detection framework allows to systematically model and quantify the signal detection behavior and failure of molecular signaling systems, and compute their key decision making parameters such as detection and false alarm probabilities. With regard to the specific TNF-NF-κB system case study in this paper and given the high involvement of the transcription factor NF-κB in cell survival, programmed cell death, immune signaling and stress response, the developed signal detection framework can serve as a useful tool to model the associated cell decision making processes. © 2023 IEEE.

关键词： Signal detection

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USDOT Tier-1 University Transportation center for Advancing Cybersecurity Research and Education 1

USDOT Tier-1 University Transportation Center for Advancing ...

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1st IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Zhang, Yunpeng Jack Wang, Kailai Song, Lingguang Lendasse, Amaury Song, Houbing Herbert Han, Zhu Verma, Rakesh Silva, Arlei Rubio-Medrano, Carlos Li, Zhixia Richard Zhang, Guohui University of Houston Dept. of Information Science Technology HoustonTX United States University of Houston Dept. of Construction Management HoustonTX United States University of Maryland Baltimore County Dept. of Information Systems BaltimoreMD United States University of Houston Dept. of Electrical & Computer Engineering HoustonTX United States University of Houston Dept. of Computer Science HoustonTX United States Rice University Dept. of Computer Science HoustonTX United States Texas A&m University-Corpus Christi Dept. of Computer Science Corpus ChristiTX United States University of Cincinnati Dept. of Civil and Architectural Engineering and Construction Management CincinnatiOH United States University of Hawai'i at Mānoa Dept. Civil and Environmental Engineering HonoluluHI United States

ISBN: (纸本)9798350319958

The Transportation Cybersecurity center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation center (UTC) funded in 2023. CYBER-CARE primarily focuses on the USDOT statutory research priority area of 'Reducing Transportation Cybersecurity Risks.' CYBER-CARE aims to establish a fundamental knowledge basis and explore advanced theory to mitigate the impacts of large-scale cyberattacks on transportation infrastructure and connected and automated vehicle (CAV) systems. The research projects at CYBER-CARE will develop conceptual frameworks, construct comprehensive datasets, explore novel analytical approaches, support the implementation of public policies and infrastructure investments, and build a high-quality industry workforce through education. All CYBER-CARE research projects can be organized into four thrusts: CAV cybersecurity, transportation data security, advanced traffic management system (ATMS) cybersecurity, and next-generation transportation cybersecurity systems. In addition, CYBER-CARE will accelerate industry collaborations, foster new technologies, and provide professionals with the skills and opportunities needed to become successful leaders in their fields. Notably, as CYBER-CARE will prioritize engagement with underrepresented minorities, these communities stand to benefit from professional development training in transportation cybersecurity. © 2023 IEEE.

关键词： technology transfer

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Discrete Cosine Stockwell Transform vs. Discrete Cosine Transform in Despeckling OCT images

Discrete Cosine Stockwell Transform vs. Discrete Cosine Tran...

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2023 IEEE International Conference on electrical, computer and Energy Technologies, ICECET 2023

作者： Al-Asad, Jawad Mewada, Hiren Flah, Aymen Patel, Amit V. Prince Mohammad Bin Fahd University Dept. of Electrical Engineering Khobar Saudi Arabia Energy Processes Environmental and E. Systems Unit University of Gabes Gabes Tunisia Charusat Space Research & Technology Center Charotar University of Science and Technology Changa India

ISBN: (纸本)9798350327816

Hard thresholding through coefficients selection and orthonormal vectors projection is investigated for Discrete Cosine Stockwell transform (DCST) and Discrete Cosine transform (DCT) towards despeckling Optical Coherence Tomography (OCT) images. The whole image is first divided into small dyadic size of sub images, then every sub image is subjected to either DCST or DCT through each of the two mentioned hard thresholding techniques. When applied to simulated and real OCT images, results show the higher efficiency of the projection technique (projecting the noisy signal onto the signal subspace) over the regular coefficients selection technique. Furthermore, projection technique is found more efficient, numerically and visually, in suppressing speckle noise than popular benchmark schemes used in OCT area such as Total Variation (TV) filter, Geometric Non-Linear Diffusion (GNLD) filter and Detail Preserving Anisotropic Diffusion (DPAD) filter. © 2023 IEEE.

关键词： DCT despeckling OCT projection Stockwell

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