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Integrated Control and Modeling of a PV-Wind Hybrid System for DC Electric Micro-Networks 3

Integrated Control and Modeling of a PV-Wind Hybrid System f...

引用

3rd ieee international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Santhosha, B.M. Negi, Harendra Singh Balaraju, V. Vekariya, Daxa Patil, Harshal Maranan, Ramya Ballari Institute of Technology and Management Visvesvaraya Technological University Department of Electrical and Electronics Engineering Ballari Karnataka India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Dr. Ntr College of Agricultural Engineering Department of Basic Engineering and Applied Sciences Andhra Pradesh Bapatla522101 India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Gujarat 391760 India Department of Computer Science and Engineering Maharashtra Pune412115 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350361186

The main objective of this work is to model, control and simulate an electrical microgrid. To achieve this objective, models of the subsystems or associated elements (generators, power interfaces, electrical loads, etc.) are built, the control strategies necessary for the integration of each subsystem in the microgrid are implemented, and it is proposed an optimization algorithm that allows the economic dispatch of the system to be carried out under different generation and demand conditions. Finally, after simulating the model, its behavior is validated in a considered operating scenario. The work environment selected to develop this project is Simulink, a tool integrated into MATLAB, due to the ease it offers when modeling and simulating dynamic systems, allowing you to customize the included libraries and export data to MATLAB for subsequent analysis. At the end of this work, it is intended to have a simulated environment to validate the behavior of the proposed microgrid, which allows considering different generation and demand scenarios, and an optimization algorithm that allows carrying out the economic dispatch of the system under the operating conditions. considered, without taking into account the randomness of generation sources and energy demand. © 2024 ieee.

关键词： Wind power

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Research on User Behavior Prediction Based on Random Forest Algorithm with RFM Model 4

Research on User Behavior Prediction Based on Random Forest ...

引用

4th international conference on computer science and Blockchain, CCSB 2024

作者： Li, Rongmin Zhang, Jingjing He, Suizhi Guangzhou Xinhua University Department of Artificial Intelligence and Data Science Guangdong Guangzhou China Shaanxi Fashion Engineering University Department of Mechanical Engineering Shaanxi Xi'an China

ISBN: (纸本)9798350356502

In the current e-commerce environment, the application of big data technology has become a key driving force for industry development. By collecting and analyzing massive user behavior data, transaction records, and market trends, e-commerce platforms can offer more personalized shopping experiences, precise product recommendations, and optimized supply chain management. Based on the Random Forest algorithm, this study establishes a prediction model for merchant coupon distribution using user consumption order data. The traditional RFM (Recency, Frequency, Monetary) customer value model is improved by combining it with the Random Forest algorithm to predict merchant coupon distribution. The research results support personalized marketing activities, optimize customer relationship management strategies, and provide targeted coupon distribution strategies. © 2024 ieee.

关键词： Random forests

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Research on Shopping Recommendation Based on Graph Neural Network and Deep Learning

Research on Shopping Recommendation Based on Graph Neural Ne...

引用

2023 ieee international conference on Electrical, automation and computer engineering, ICEACE 2023

作者： Xie, Wenyu Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350309614

For anonymous or new users on shopping websites, when their historical interaction information is unknown, a model can be established based on the user's historical behavior sequence data to mine their long-term interests and short-term preferences, and predict the items that the user may click on in the future. Then, based on the shopping sequence, this paper recommends a collection of items to purchase next. Inspired by the graph neural network, this network is used to establish a connection model between shopping items. This method can capture the transformation relationship between items and obtain the correlation between nodes and their neighboring nodes in the conversation graph. Through this approach, noise interference from unrelated neighboring nodes can be reduced and recommendation performance can be effectively improved. In order to establish a short-term interest model for users, a fixed size mobile window was designed to integrate the user preferences of the last few consecutive shopping baskets. By using a recurrent neural network, we fuse the basket representation, global, and short-term user shopping preferences to obtain the final recommendation result. The experimental results on two public datasets show that the algorithm can effectively improve the recommendation accuracy of the next shopping basket. © 2023 ieee.

关键词： Artificial intelligence computer algorithms Deep learning Graph neural network Sequence recommendation Sliding window

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Neural Network-Based Load Forecasting for Power Grids 9

Neural Network-Based Load Forecasting for Power Grids

引用

9th international conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Deepika, D.S. Krishnasami, Umamaheswari Rajavinu, N. Vithya, V.T. Manikandaprabu, P. Sathish, T. Velammal Institute of Technology Department of Computer Science And Engineering Tamil Nadu Panchetti601204 India Dr. Mahalingam College of Engineering And Technology Department of Electrical And Electronics Engineering Tamil Nadu Pollachi642003 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Chennai600077 India Rmk College of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Puduvoyal601206 India Sona College of Technology Department of Mechanical Engineering Tamil Nadu Salem36005 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350365092

This exploration paper delves into the realm ofcargosoothsaying for power grids, presenting anew approachpredicated on neural network methodologies. cargosoothsaying, a critical aspect of power gridoperation, is imperative foricing optimal resource allocation, grid stability, and energyeffectiveness. The proposed methodology leverages the capabilities of neural networks to capture intricate patterns and dependences with in literalcargo data. Throughexpansive training and confirmation, the neural network model demonstrates its proficiency inprognosticating unborncargo demands with high delicacy. It evaluates the effectiveness of the neural network grounded cargo soothsaying against traditional styles, showcasing its rigidity to dynamic and nonlinearcargo patterns. The findings contribute to the advancement of cargosoothsaying ways, emphasizing theeventuality of neural networks to enhance the perfection and trustability of prognostications in the complex and evolvinggeography of power gridoperation. © 2024 ieee.

关键词： Machine learning

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A Deep Learning Method to Translate Phonocardiogram (PCG) Signals to Electrocardiogram (ECG) Waveforms 13

A Deep Learning Method to Translate Phonocardiogram (PCG) Si...

引用

13th ieee international conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2023

作者： Chen, Wei Li, Zhi Zhang, Guanglie College of Computer Science and Software Engineering Shenzhen University Shenzhen China City University of Hong Kong Department of Mechanical Engineering Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China

ISBN: (纸本)9798350315196

In clinical practice, an electrocardiogram (ECG), which analyzes the electrical activity of the human heart, is a common cardiovascular diagnostic test. The ECG data can be stored and diagnosed effectively. However, precise positioning of the ECG electrodes is necessary for a reliable ECG tracing. This has an impact on how ECG monitors are used to check on patients at home. On the other hand, the Phonocardiogram (PCG) is a standard method for quantifying the properties of heart sounds in order to monitor and assess cardiac activity. With the advancement of wearable sensors, it is now simpler to record the heart's sound. We proposed a deep learning approach to translating PCG signals to ECG waveforms. This technique can be utilized to monitor cardiovascular disease in consumer wearable device development. The proposed method is evaluated on a benchmark dataset of normal and anormal cardiac data. Experimental results show that our proposed approach is reasonable and effective. The generated ECG outputs can achieve a high accuracy, and the root mean squared error (RMSE) is 0.053. © 2023 ieee.

关键词： Electrocardiograms

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Automated Bengali abusive text classification: Using Deep Learning Techniques 1

Automated Bengali abusive text classification: Using Deep Le...

引用

1st ieee international conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Titli, Showni Rudra Paul, Shimul National Institute of Technology Dept. Electronics and Communication Engineering Calicut India Ahsanullah University of Science and Technology Dept. Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350348057

In recent years, the breakdown of most human causes that hampers a large number of people is verbal abuse. The most noticeable fact is that most of the verbal abuse is made online by people behind the screen. While this problem has occurred with growing modern technologies, a modern solution is needed to solve the drawback. The growing rate of abusive comments and hate speech online is rapidly increasing on a large scale, and manual reports and corrections cannot help this critical situation. This proposed model introduces automation of the hate speech filtering process through deep learning. This work uses only Bengali datasets to find the real classification, as many Bengali and English mixed research works are available in this field. Using the intelligent automated classification of text comments in a limited resource-constrained language (example: Bengali) is critical for several reasons. This proposed system classifies 'Personal Offensive Text', 'Geographical Offensive Text', 'Religious Offensive text', 'Crime Offensive Text', 'Entertainment, Sports, Meme Tiktok, and others'. By combining two large datasets for this research and employing Bengali BERT, the best feasible outcome has been *** paper indicates the complete results of the combined two different datasets and classified into nine segments, while the proposed Bengali BERT models achieved the highest accuracy of 0.706 and a weighted f1 score of 0.705 in the identification and classification tasks. © 2023 ieee.

关键词： automation

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An Integrated Aircraft Maintenance Training Platform Based on 'Simulation Training+Auxiliary Operation'

An Integrated Aircraft Maintenance Training Platform Based o...

引用

2023 international conference on computer science and automation Technology, CSAT 2023

作者： Zhang, Wei Zhu, Dan Xie, Yanfu Aviation Maintenance Nco Academy Air Force Engineering University Xingyang China

ISBN: (纸本)9798350371437

In view of the development of large, complex and technology-intensive aircraft, it has increased the difficulty of maintenance. A solution of integrated aircraft maintenance training platform based on 'simulation training + auxiliary operation' is proposed. The architecture adopts the interface navigation control strategy based on the interface transformation matrix, and uses the non-secret surrogate image (two-dimensional code image) to obtain the identification information, which ensures the accurate positioning. The maintenance training concept, advanced technology and design scheme adopted in the research and development process of this project will certainly play a positive role in promoting the innovation of maintenance training means and the reform of training mode. © 2023 ieee.

关键词： Maintenance

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Development of a Verilog-Based System for Pattern Detection Using Convolutional Neural Networks 2

Development of a Verilog-Based System for Pattern Detection ...

引用

2nd international conference on Recent Trends in Microelectronics, automation, Computing, and Communications Systems, ICMACC 2024

作者： Kandula, Bala Sindhuri Singh, Sangeeta Tata, Subhashini Manasaveena, T. Bolisetty, V Vijayasri Sadguna Kumari, V. S.R.K.R Engineering College Department of E.C.E Bhimavaram India Vardhaman College of Engineering Hyderabad Department of E.C.E Telangana India SR Gudlavalleru Engineering College Department of E.C.E Gudlavalleru India NRI Institute of Technology Department of E.C.E Vijayawada India Aditya College of Engineering and Technology Department of E.C.E Surampalem India Pydah College of Engineering Department of E.C.E Kakinada India

ISBN: (纸本)9798350366570

Pattern detection is a foundational element in computer vision, crucial for advancements in autonomous driving, surveillance, and numerous other fields. Convolutional Neural Networks (CNNs) have significantly enhanced pattern detection by improving both accuracy and efficiency. This paper aims to develop a Verilog-based system for pattern detection using CNNs, focusing on detecting and counting occurrences of a specified test pattern within input images. The CNN architecture employs Convolutional Layers for feature extraction and Max Pooling Layers for spatial downsampling, both essential in image processing. Through iterative weight adjustments during training, the CNN learns to recognize patterns similar to the test pattern. After training, the system evaluates unseen images, predicting the presence and count of test pattern instances. To optimize performance, the system incorporates approximate adders to reduce area and gate count, thereby enhancing efficiency. These optimizations enable the system to achieve high performance while maintaining computational efficiency, providing detection and counting of specified patterns in images. This capability offers valuable insights and practical solutions for various real-world applications. © 2024 ieee.

关键词： Convolutional neural networks

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Spectrogram Features and Xception Classifier Based Audio Spoof Detection System

Spectrogram Features and Xception Classifier Based Audio Spo...

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2024 international conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Chakravarty, Nidhi Dua, Mohit Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350388534

Voice biometric authentication has gained significant attention in recent years due to its non-intrusive and user-friendly nature. In this research paper, we present a comprehensive study on the effectiveness of three different spectrograms - Constant Q Transform (CQT), Mel and Gammatone at the front end of an Audio Spoof Detection (ASD) system. The back end of the system employs the Xception pretrained model. Our experimental results reveal that Gammatone Spectrograms outperform both CQT Spectrograms and Mel Spectrograms in terms of performance. Specifically, combination of Gammatone Spectrogram with Xception pretrained has achieved a remarkable 3.5% and 2% Equal Error Rate (EER) for ASVspoof 2021 DF and DECRO dataset, respectively © 2024 ieee.

关键词： Spectrographs

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Predicting Employee Churn- Data Driven Approach for Retention Management

Predicting Employee Churn- Data Driven Approach for Retentio...

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2024 international conference on System, Computation, automation and Networking, ICSCAN 2024

作者： Jayaraj, R. Mehrotra, Safal Kumar, Harshit School of Computing Srm Institute of Science and Technology Dept. of Data Science and Business Systems Chennai India

ISBN: (纸本)9798331510022

Employee churn is a critical challenge faced by organizations across industries, leading to disruptions in productivity and increased costs associated with recruitment and training. In this study, we propose an effective solution leveraging Random Forests to predict employee churn accurately and efficiently. Our approach addresses the inherent complexities of employee turnover prediction while considering resource constraints commonly encountered in practical applications. The proposed model incorporates feature engineering techniques and ensemble learning principles to harness the predictive power of Random Forests. Through a comprehensive evaluation on real-world employee datasets, we demonstrate the superior performance of our model in comparison to existing state-of-the-art methods. Our analysis encompasses various evaluation metrics, including accuracy, precision, recall, and F1-score, showcasing the robustness and reliability of our model across diverse organizational contexts. © 2024 ieee.

关键词： Contrastive Learning

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