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Conventional Current Reference Generation Strategies for Grid-Connected Distributed Energy Sources

Conventional Current Reference Generation Strategies for Gri...

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2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Joshi, Jyoti Dubey, V.P. Kandpal, Jyoti Chamoli, Vivek Graphic Era Hill University Dept. of Computer Science and Engineerring Dehradun India Graphic Era Hill University Dept. of Electronics and Communication Dehradun India

ISBN: (纸本)9781665474924

Low voltage ride through (LVRT) is the ability of a photovoltaic (PV) system to maintain continuous operation during grid voltage dips or short-term power outages. To meet the LVRT requirements, PV systems typically include advanced inverter technology, energy storage systems, and additional protection devices. Current reference generation is a key aspect of LVRT technology, as it allows the inverter to maintain a stable current reference during voltage dips and outages. This is achieved by using advanced algorithms to control the inverter's output current, and by incorporating voltage and frequency monitoring systems to detect grid disturbances. This research work reviews the performance of various current control systems under imbalanced grid faults and abrupt changes in irradiance. To confirm the behaviour of the current control mechanisms, simulation outcomes are shown. © 2023 IEEE.

关键词： Electric current control

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Traffic Prediction Using Graph Neural Network

Traffic Prediction Using Graph Neural Network

引用

2023 IEEE Guwahati Subsection Conference, GCON 2023

作者： Sri, Lakshman S V Karthick, A. Akash, S. Anuradha, R. Sri Ramakrishna Engineering College Dept. of Aeronautical Engineering Coimbatore India Sri Ramakrishna Engineering College Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350337785

Traffic prediction is an important and challenging task in transportation management. Accurately predicting traffic patterns is crucial for reducing congestion, improving safety, and optimizing travel time. In recent years, graph neural networks (GNNs) have shown great potential in predicting traffic by taking into account the graph structure of road networks. In this project, we propose to develop a traffic prediction system using GNNs. The proposed system consists of three main components: data pre-processing, model development, and model evaluation. We first collect traffic data from various sources and pre-process it to remove noise and inconsistencies, and convert it into a suitable format for GNNs. We then develop a GNN model designed to capture the graph structure of the traffic network and use it to predict traffic patterns for a given time interval. The proposed model is trained and optimized using a loss function and evaluated using standard metrics such as root mean squared error (RMSE). The proposed traffic prediction system has the potential to significantly improve traffic management and reduce congestion by providing accurate traffic predictions. The GNN model can capture complex traffic patterns and dependencies between different parts of the road network, leading to more accurate predictions. Overall, this project aims to provide a better understanding of the potential of GNNs in traffic prediction and contribute to the development of more efficient and effective transportation management systems © 2023 IEEE.

关键词： Mean square error

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Enhancing Flight Delay Predictions Using Advanced Machine Learning and Data Analytics

Enhancing Flight Delay Predictions Using Advanced Machine Le...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mawada Abdelrazig.k Abdalla Ahmed Lawgali dept. Computer Science University of Benghazi Benghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Flight delays are a significant issue in the aviation industry, affecting reputations, individual schedules, and a country's economy. This study aims to develop an accurate machine learning model to predict flight delays rom the U.S. Department of Transportation and weather data from the National Oceanic and Atmospheric Administration (NOAA). Various machine learning algorithms, including Random Forest, Gradient Boosting Machine, Logistic Regression, Bagging Classifier, AdaBoost, and Stacking Classifier, were evaluated using multiple data handling techniques such as oversampling and undersampling. The Random Forest model, particularly on the oversampled dataset, demonstrated the highest performance, achieving an impressive 95% accuracy. This result highlights the effectiveness of advanced data handling and ensemble methods in improving predictive accuracy as well as the value of integrating multiple algorithms and data strategies to enhance the reliability and precision of flight delay predictions.

关键词： Accuracy Machine learning algorithms Data handling Atmospheric modeling Transportation Predictive models Delays Ensemble learning Random forests National Oceanic and Atmospheric Administration

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Adchat-TVQA: Innovative Application of LLMs-Based Text-Visual Question Answering Method in Advertising Legal Compliance Review

Adchat-TVQA: Innovative Application of LLMs-Based Text-Visua...

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Machine Learning and computer Application (ICMLCA), International Conference on

作者： Ruiling Gao Yingqi Hou Weijie Tang Jin Zhang Yanbo Hu Haiyun Huang Wen'an Tan Liping Li Dept. of Computer Engineering Shanghai Polytechnic University Shanghai China Dept. of Computer Science Donghua University Shanghai China Dept. of Computer Science Tufts University Medford MA USA

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

Advertising legal compliance reviews have always been time-consuming and labor-intensive, and existing Large Language Models(LLMs) are far worse performing than senior industry experts. In this paper, we propose a novel LLMs-based text-visual question answering method called Adchat-TVQA for advertising legal compliance review. After reading an advertising image and the corresponding review question, it will first understand the content on the image through multimodal learning, then optimize the question with Zero-Shot Chain-of-Thought (CoT) prompting method based on predefined industry expert experience, and finally input the augmented question into the large language model for inquiry. The feasibility of this method is verified through system prototype implementation and end-to-end functional tests. In addition, we summarized the characteristics of advertising images and added image segmentation and text box transpose to the data processing process for system performance improvement. Performance testing of the text visual cognitive question task on the AIWIN2021 dataset shows that the method scores higher than Microsoft's LayoutLM, with an increasement of the score from 51.52% to 52.74%.

关键词： Industries Visualization Reviews Law System performance Prototypes Question answering (information retrieval) Prompt engineering Advertising Testing

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ADAMAX-Based Optimization of Efficient Net V2 for NSFW Content Detection 1

ADAMAX-Based Optimization of Efficient Net V2 for NSFW Conte...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Arora, Chayanika Raj, Gaurav Ajit, Akshat Saxena, Aditya Sharda University Greater Noida Sharda School of Engineering and Technology Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350335774

In recent years, the need for automatic detection of Not Safe for Work (NSFW) content on social media platforms has increased dramatically. In this study, we contrasted the presentation of five optimizers, namely ADAM, ADAMAX, ADAMW, SGD, and ADAGRAD, on the EfficientNet-V2M and V2L models for NSFW content detection. We used a dataset consisting of NSFW images for training and testing the models. The results show that the ADAM optimizer performed better than the other optimizers with an accuracy of 98.80% for training and that for testing is 95.60% for the EfficientNet-V2L model. However, all the optimizers performed reasonably well with same parameters. This study provides valuable insights into the selection of optimizers for NSFW content detection using deep learning models. The dataset used in the research consists of a large number of images with explicit and non-explicit content. The results were evaluated based on accuracy, precision, recall, F1-score, loss, and area under the curve (AUC). The findings indicate that ADAM, ADAMAX, and ADAMW outperform the other optimizers in terms of all evaluation metrics. With the easy availability of explicit content online, it has become a concern for society, especially for children who are easily influenced by such content on various platforms. Exposure to unfiltered and inappropriate content can have a negative impact on young minds. To safeguard against such content, the authors of this paper review and analyze different approaches for detecting and filtering pornographic and NSFW content. The objective of this paper is to provide a filtered and safe content environment for the community, especially for children and teenagers who are the most vulnerable. © 2023 IEEE.

关键词： Deep learning

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Machine Learning Techniques for Predicting Ovarian Cancer in its Early Stages Using Biomarkers

Machine Learning Techniques for Predicting Ovarian Cancer in...

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International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Mir Jafikul Alam Jannat-E Anawar Khandaker Mohammad Mohi Uddin Md. Hasibur Rahman Md. Mahbubur Rahman Dept. of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

The deadliest gynecological cancer affecting women is ovarian cancer, currently incurable with no effective medication treatments. The key focus of this research is to assess insights for early diagnosis using statistical analysis and machine learning techniques on data from clinical trials obtained from 349 patients. Several techniques, including Random Forest, Decision Tree, Gaussian NB, AdaBoost, and Logistic regression, were applied to find the most reliable factor for ovarian cancer prediction. A clinically evaluated raw dataset of benign samples and malignant ovarian tumor patient data set is used to develop early-stage ovarian cancer predictions, and the effectiveness of ML models was examined utilizing metrics including F1-score, Accuracy, Precision, and Recall. The proposed study shows better outcomes, with the Random Forest classifier exhibiting the highest accuracy for validation at 99% based on the test data of ovarian cancer predictions. Even though early-stage ovarian cancer detection is generally unavailable, cancer diagnosis may be greatly aided by machine learning.

关键词： Measurement Logistic regression Statistical analysis Forestry Predictive models Reliability Random forests

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A Comprehensive Study on Accident Detection Approaches

A Comprehensive Study on Accident Detection Approaches

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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Donepudi, Harshini S Saxena, Mayank Yun, Yeo Xiong Jansirani, Preethaa Sethi, Simardeep Singh Banik, Barnali Gupta Andhra University Dept. of Computer Science Sys Engineering Andhra Pradesh Visakhapatnam India University of Allahabad Dept. of Computer Science and Engineering Uttar Pradesh Allahabad India National University of Singapore Dept. of Engineering Science Singapore Singapore College of Engineering Guindy Anna University Dept. of Electronics and Comm. Engineering Chennai India Guru Tegh Bahadur Institute of Technology Dept. of Computer Science and Engineering New Delhi India C. R. Rao Advanced Institute of Mathematics Statistics Computer Science University of Hyderabad Telangana India

ISBN: (纸本)9781665458832

Accident-prone detection predicts the likelihood of a vehicle getting into an accident. Major contributing factors to road accidents include distracted driving, unsafe road infrastructures, and social sensitivity features, but not only these factors. The data collected by monitoring the traffic is used to model and test the detection systems. This behaviour is usually identified utilising exploratory data analysis of historical data based on previous accidents, traffic patterns, the infrastructure of the roads and other miscellaneous factors. In this paper, existing techniques are studied in detail to weigh the benefits and costs of implementing different detection models. This study aims to determine how various model(s) can be implemented to ensure a safe commute that can eventually lead to real-time alerts to prevent fatal accidents. The notion of lane-less and lane-based traffic is also considered. Specifically, a Long Short-Term Memory (LSTM) network has been reviewed, and notable enhancements have been suggested. After arriving at the conclusions, the future scope has also been discussed. © 2022 IEEE.

关键词： Data mining

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Generalization Evaluation of Seq2seq based Fog Computing Application Placement Algorithm on Limited Dataset 3

Generalization Evaluation of Seq2seq based Fog Computing App...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Pakpahan, Michael S. M. Nugroho, Lukito Edi Widyawan, Widyawan Wardhana, Ajie Kusuma Muhammad Ardian, R.A. Astagenta, Rangga Satria Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Dept. Informatics Yogyakarta Indonesia

ISBN: (纸本)9798350394559

Addressing its growing number and vital role, decentralization of cloud computing becoming a necessity. Fog computing aims to bring application closer to the data source-typically at the network's edge by leveraging local resources to provide faster data processing and decision-making. Fog computing then has to place application strategically to use its limited fog resource to improve application performance metrics. This problem known as Fog Application Placement Problem (FAPP) has been approached using previous methods that rely on rules and prior knowledge that may not be adaptive, but rather being overly specialized to specific problems. Deep learning with its learning mechanism, can offer more adaptable and dynamic solutions for a wide range of scenarios, especially in fog network that continuously evolve. This research investigate Seq2seq placement model inherent limitations, notably the impracticality of generating every possible pattern from all potential request configurations. We aim to address and answer the following critical questions: 1) How does the model's performance vary when confronted with unseen requests or an augmented number of modules, especially considering the limitations in training data?;2) Can the seq2seq model, even with its training limitations, adhere to the heuristic rules of the dataset when dealing with unfamiliar problems? This research shows that in similar availability, there is a reduction of almost half of the response time with 183.03 ms, 2.93 number of hops, and 0.87 megabyte of transmitted messages against the hop3 algorithm. Moreover, we highlight the ability of seq2seq model to follow heuristic rules in unseen scenarios. © 2023 IEEE.

关键词： Fog computing

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Foundations of the Fast Galois Field Arithmetic 6

Foundations of the Fast Galois Field Arithmetic

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6th IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics, UkrMiCo 2023

作者： Petryshyn, Mykhailo Petryshyn, Lubomyr Precarpathian National University Dept. of Computer Science and Information Systems Ivano-Frankivsk Ukraine AGH University of Krakow Dept. of Enterprise Management Krakow Poland

ISBN: (纸本)9798350348484

The effectiveness of utilizing theoretical- numerical transformations in Galois code systems has been substantiated based on an analysis of known methods of data transformation and digital processing. The drawbacks of dynamic execution of arithmetic operations in binary codes are identified, along with methods for eliminating them during Galois encoding, resulting in increased digital processing speed. The research utilized recursion theory, cyclic coding methods, discrete transformations, and digital automata. A novel method for parallel execution of arithmetic operations in Galois fields, which eliminates inter-digit transitions, has been proposed. Mathematical models for executing arithmetic operations have been developed, accompanied by a structural diagram and an explanation of the principles behind arithmetic operations in Galois codes. A comparison was made between known and proposed methods, revealing that the proposed method reduces conversion time by 6 to 25%. The use of recursive data coding methods with Galois codes has enhanced information exchange security while improving data processing speed and efficiency. The development of data processing methods in Galois field, along with the implementation of Galois arithmetic processors, hinges on ensuring high computational environment homogeneity. This, in turn, simplifies their implementation in microelectronic performance, increases operational reliability, and reduces the cost of implementing and operating information processing systems. © 2023 IEEE.

关键词： Numerical methods

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Feature Selection Using Evolutionary Techniques

Feature Selection Using Evolutionary Techniques

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gholami, Mandana Mouhoub, Malek Sadaoui, Samira University of Regina Dept. of Computer Science ReginaSKS4S 0A2 Canada

ISBN: (纸本)9798350337020

Data clustering has many applications in machine learning, data mining and image processing. K-means is the most popular clustering algorithm due to its efficiency and simplicity of implementation. However, K-means has limitations, such as large feature spaces, which may affect its effectiveness. To improve K-means accuracy, we adopt the Biogeography-Based Optimization (BBO) evolutionary technique to select the most relevant features of datasets. We conducted several experiments to compare our approach with other methods, such as PCA and Particle Swarm Optimization (PSO). The results demonstrate the effectiveness of BBO for feature selection. © 2023 IEEE.

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