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Experimental investigations into common mode impedance of PV panels

Experimental investigations into common mode impedance of PV...

引用

Renewable Power Generation and Future Power Systems Conference 2023, RPG 2023 UK

作者： Kane, Makarand M Taylor, Nathaniel Mansson, Daniel School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781839539848

The nature of common mode (CM) parasitic impedance (ZPV) of photovoltaic (PV) panels is investigated by experimentation. Measurements are done by two methods: (a) time domain signal recording using a signal generator and an oscilloscope (b) frequency sweep using an LCR meter. It is shown that ZPV is not purely capacitive for the frequency range from 50 Hz to 1 MHz. It is also shown that the total common mode impedance in a PV installation is affected by the nature of ZPV. The frequency spectrum of CM current would be different if ZPV is not purely capacitive. This would affect the design of CM filters on the DC and AC side and also equipment like PV emulators, DC line impedance stabilization network, etc. © The Institution of engineering & technology 2023.

关键词： Leakage currents

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Practical Deep Learning Models for QIM-based VoIP Steganalysis 24

Practical Deep Learning Models for QIM-based VoIP Steganalys...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Cheng School of Computer Science and Technology Engineering Research Center of Mine Digitalization China University of Mining and Technology Ministry of Education Jiangsu Xuzhou China

ISBN: (纸本)9798400706868

Quantization index modulation (QIM) based VoIP steganography can conceal secret information in VoIP streams. Malicious users could use this technology to conduct illegal activities, threatening network and public security. Hence, practical steganalysis models that could detect QIM-based VoIP steganography are urged to be developed. In recent years, deep learning (DL) models have been investigated for this task, and exciting outcomes have been achieved. However, existing models are far from practical. Two major challenges are required to be addressed. First, there is still significant room for improvement in detection accuracy. Second, studies that balance the detection accuracy and response time are still insufficient. In this context, our main research topic fits in the QIM-based VoIP steganalysis theme, which aims to detect QIM-based steganography in VoIP streams in a fast and accurate manner. © 2024 ACM.

关键词： covert channel

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Machine Learning Based Personality Classification for Carpooling Application

Machine Learning Based Personality Classification for Carpoo...

引用

2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Anas, Mohd Gunavathi, C. Kirubasri, G. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Sona College of Technology Department of Computer Science and Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350335835

Sharing a ride in vehicle is known as carpooling and many businesses are adapting this system. Carpooling is an effective way to control air pollution since it decreases the number of automobiles on busy roads. It also helps to reduce traffic roadways, allowing automobiles to drive faster. Carpooling reduces the pollution even further because vehicles use less gas when they travel without much stopping in the traffic. People may also drive their automobiles whenever they choose, with no need for cheaper maintenance and parking charges for each vehicle. According to studies, each car in a carpooling system takes up to 11 vehicles off the road. This cuts yearly pollution by 13 metric tonnes. In India, if carpooling done correctly, one single car will take around 25 automobiles off the road. To make carpooling easier, in this work, a machine learning (ML) model is utilized to identify the personality types of customers based on the tweets and to group them for the same car with similar personalities. The idea intends to break down this barrier between people who don't like to travel with the people who have different personality. In this model, Natural Language Processing (NLP) is used to deal with the dataset before passing it to the model to train. The proposed work is done using XG-Boost, Decision Tree Classifier, Support Vector Machine, and Stochastic Gradient Decent optimizer. Pre-processing the dataset employed the NLP techniques such as Tokenization, Lemmatization and Stemming. After evaluating all the algorithms, we found out that XG-Boost is outperforming all the remaining three algorithms by achieving 68% accuracy easily with 5-fold cross validation. © 2023 IEEE.

关键词： Stochastic systems

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EEG Based Thought-to-Text Translation Via Deep Learning 7

EEG Based Thought-to-Text Translation Via Deep Learning

引用

7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Ali, Sarfraz Mumtaz, Wajid Maqsood, Aiman School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan

ISBN: (纸本)9798350338461

Major contributors to impairment are neurological conditions such epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease, and others. These neurodegenerative conditions are life-altering, and those afflicted with them must fight a new battle every day. This manuscript presents the implementation of a Thought-to-Text Conversion BCI system. The system employs a joint CNN-LSTM deep neural network to learn high-level features from MI-EEG signals. To eliminate various artifacts from EEG signals, pre-processing is employed, such as utilizing a band-pass filter. Although an Auto-encoder layer is included, it has been found to decrease efficiency. The XGBoost Classifier is utilized to classify EEG signals into five EEG imagery tasks. This Deep Learning model is then pickled using Python and deployed to the server. The server prompts the development of a GUI that maps the input EEG signals to corresponding alphabets. The Neural Network is trained on multiple open-sourced EEG data-sets, utilizing various optimization algorithms to achieve high system accuracy and desirable efficiency for real-time applications. The textual output obtained can be utilized to aid in the rehabilitation of paralysis and stroke patients. The proposed hybrid model achieved an accuracy of 96.89% on testing *** taking approximately 6 seconds per letter to be typed on the GUI screen. © 2023 IEEE.

关键词： Electroencephalography

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Suicide: An Analysis and Prediction Using Pyspark and Machine Learning Models

Suicide: An Analysis and Prediction Using Pyspark and Machin...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Akhila, Pujala Priyadarshini, R. Banerjee, Shalini Thomas, Beryl Abey School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350305708

The World Health Organization (WHO) reported in 2021 that more over 700, 000 people had committed suicide. Suicide can be stopped, but most efforts have so far been ineffective. However, the application of machine learning presents fresh chances to improve prediction accuracy and advance the cause of suicide prevention. This study uses data-driven methodologies to analyze and predict suicide, and presents the same in this report. Machine learning algorithms and PySpark programs are used to analyze a dataset of demographic, socioeconomic, and psychological characteristics related to suicide instances from a Kaggle dataset comprising of 237519 rows and 7 columns. The study creates prediction models like linear regression, decision tree, decision tree regressor and bagging regressor. The results support efforts to prevent suicide by assisting in the prioritization of resources and focused interventions. © 2023 IEEE.

关键词： bagging decision tree linear regression prediction models pyspark suicide analysis

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LeakGAN-Based Causality Extraction in the Financial Field

LeakGAN-Based Causality Extraction in the Financial Field

引用

作者： Sun, Zhengyan Li, Xiaoqing Zhu, Guangli School of Computer Science and Engineering Anhui University of Science and Technology Huainan232001 China

Causality extraction model can quickly extract causality in text. It can be applied to event prediction, question-answering systems, and scenario generation. The traditional causality extraction pays more attention to the extraction of entities and ignores the deep semantic representation between cause entities and effect entities of the text. So the accuracy of causality extraction is low in the end. A causal relationship extraction model based on LeakGAN is proposed to solve this problem. The core task of the model is to analyze the causality existing in the review text (this paper focuses on explicit one-cause and one-effect analysis). It also realizes deep extraction under semantic enhancement. Four levels of causality extraction model based on LeakGAN are proposed: data preprocessing, pre-training, Bi-LSTM + Attention, and LeakGAN learning. Experimental results show that the model can improve the accuracy of causality extraction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Extraction

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Efficient Implementation of a Fully Analog Neural Network on a Reconfigurable Platform 66

Efficient Implementation of a Fully Analog Neural Network on...

引用

2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Ige, Afolabi Hasler, Jennifer School of Eletrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798350302103

This paper investigates the potential of floating gate field-effect transistors (FETs) as primitives for subthreshold computation in analog neural networks. By leveraging the inherent properties of these transistors, we demonstrate their suitability for constructing neural network activation functions, such as sigmoid and rectified linear units (ReLUs), as well as winner-take-all (WTA) circuits for softmax activation. Our end-to-end analog implementation successfully classifies the concentric circles problem, illustrating the advantages of maintaining an analog signal chain throughout the process. © 2023 IEEE.

关键词： Analog computing Analog neural networks Computing-in-memory

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Quantum State Tomography in FPGA

Quantum State Tomography in FPGA

引用

Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Miller, Nathan Eli Mukhopadhyay, Saibal School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

We demonstrate an efficient s olution f or q uantum s tate t omography implemented on FPGA. By observing the closed-form solution for complete measurement sets, we create a simple, accurate, reconfigurable solution fo... 详细信息

ISBN: (纸本)9781957171272

关键词： Age related vision changes Bismuth Crosstalk Field programmable gate arrays Quantum computation Refractive index

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A Non-Destructive Method to Estimate Leaf Water Content Using the Ka-Band

A Non-Destructive Method to Estimate Leaf Water Content Usin...

引用

2023 IEEE International Opportunity Research Scholars Symposium, ORSS 2023

作者： Contreras, Joshua Goodnight, Ashley Parker, Quinn Simon, Nia Theumer, Jonas Zajić, Alenka Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9798350337006

In this paper we demonstrate the use of Ka-band microwave transmission for remote measurement of relative water content (RWC) in leaves. The system, consisting of commercially available horn antennas connected to a vector network analyzer, offers a simple, inexpensive, and low-energy design compared to other non-destructive methods like terahertz spectroscopy. In addition, Ka-band frequency electronic components require less power and can be used in a wider range of environments. Using leaf samples from the Spathiphyllum Wallisii plant, we derived a model that accepts the power transmitted through the leaf as input and outputs an estimate for the RWC and a recommendation on whether or not to water the plant. We verify our model by comparing it to RWC values obtained from a standard method based on weighing the leaf and soaking/dehydrating it. Results indicate that the approach is valid with acceptable model performance. Further work is proposed to improve robustness and accuracy. © 2023 IEEE.

关键词： Remote sensing

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Machine Learning-based Framework for Malware Detection in Critical Infrastructures for Smart Cities

Machine Learning-based Framework for Malware Detection in Cr...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Bhatt, Aditya Dasadiya, Sujal Gohil, Aditya Gupta, Rajesh Kumar Jadav, Nilesh Tanwar, Sudeep Garg, Deepak Nirma University Institute of Technology Department of Computer Science and Engineering Ahmedabad India SR University School of Computer Science and AI Telangana Warangal India

ISBN: (纸本)9798350361537

technology is an inseparable part of today's world and is extensively used in every conceivable domain to provide a wide range of applications. technology has many critical infrastructures, which serve the function of delivering crucial services essential for the smooth functioning of smart cities. These infrastructures produce a large amount of data which is extremely sensitive and very potentially catastrophic if it falls into the wrong hands. Various techniques and concepts have been introduced and developed to deal with the detection of malicious data. But with the passing time, malware threats grow in both frequency and complexity, machine learning and artificial intelligence techniques can be combined to enhance malware detection. Many Machine Learning algorithms exist which may be confusing for users. Different algorithms vary in their results, accuracy, and prediction capabilities depending on the data they are trained and tested on. For data produced by critical infrastructures, the prediction must be as accurate as possible. Encouraged by the aforementioned facts, we propose an approach aiming to contrast and compare different machine learning algorithms for malware detection on portable executable files dataset. Different performance metrics are compared for all the considered algorithms, based on which, the best algorithm for critical infrastructure malware detection can be determined. © 2024 IEEE.

关键词： Adversarial machine learning

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