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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Weihang Ren Mingze Li Lei Fan Yongpei Guan Dept. of Industrial and Systems Engineering University of Florida FL USA Dept. of Electrical and Computer Engineering University of Houston TX USA Engineering Technology University of Houston TX USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks.

关键词： Runtime Scalability Power systems Planning Security Optimization AC power transmission Load flow

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A New Intrinsic Complexity Metric for Air Traffic De-Conflicting 43

A New Intrinsic Complexity Metric for Air Traffic De-Conflic...

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43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024

作者： Zombré, Patrice Delahaye, Daniel Tapamo, Jules-Raymond Olanweraju, Oludolapo A. Mora-Camino, Felix Air Navigation Asecna Dakar Senegal Optimization and Machine Learning Lab. Ecole Nationale de l' Aviation Civile-ENAC Toulouse France University of KwaZulu-Natal-UKZN Computer Eng. Dept. Durban South Africa Durban University of Technology-DUT Industrial Eng. Dept. Durban South Africa Universidade Federal Fluminense-UFF Dut and Enac Industrial Eng. Dept. Niteroi Brazil

ISBN: (纸本)9798350349610

This study in in the field of Air Traffic Flow Management and considers mainly the pre-tactical planning level of air traffic flows where their global balance is already fixed (flows structures and levels, control sectors and capacity) and where flights are treated on an individual basis in the space between the different departure/arrival airports. Then, for each flight, decisions such as departure rescheduling and/or rerouting may be taken by the Air Traffic Flow Management to cope with predicted traffic congestion situations resulting in conflicts. The adopted objective is the global reduction of interactions between flights while maintaining as much as possible the original efficiency of flight plans computed by airlines. A measure of the criticality of the separation between two aircrafts is introduced as a basis to construct new air traffic complexity measures, either local or global, either instantaneous or integrated over periods of time. It is shown in this study how the resulting metric can be used to perform through standard maneuvers the de-confliction of enroute air traffic over a given area and during a given time window. Two approaches are considered, one which is global and leads to the resolution of a large nonlinear optimization problem, and another one which is decentralized and makes use of directional components of the aircraft centric and flight centric complexity indexes. Then, in the case of flight centric traffic control, using the new flight centric complexity measure, the assignment of flights to the different controllers in charge of the monitoring of an extended area is formulated as an optimal partitioning problem where interactions between controllers must be minimized. To cope quickly with this combinatorial problem, a heuristic approach is developed. © 2024 IEEE.

关键词： Traffic congestion

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Implementation of an AI-based Competitive Cheerleading Coaching Assistant System

Implementation of an AI-based Competitive Cheerleading Coach...

引用

2023 IET International Conference on Engineering Technologies and Applications, ICETA 2023

作者： Yang, Chun-Hung Tu, Jiun-Yu Wu, Dong-Shing Zhuang, Ya-Ting Tsai, Pei-Ying Hsu, Chen-Kai Dept. of Electronic Engineering Southern Taiwan University of Science and Technology No.1 Nantai St. Yungkang Dist. Tainan City710301 Taiwan Dept. of Computer Science and Information Engineering Southern Taiwan University of Science and Technology No.1 Nantai St. Yungkang Dist. Tainan City710301 Taiwan

ISBN: (纸本)9781839539848

Competitive cheerleading is a highly complex and intense sport characterized by intricate motions. The complexity and high intensity of the sport often lead to various sports injuries. Therefore, the role of coaches is crucial in the training process of competitive cheerleading. However, due to the insufficient number of coaches, there are inevitably challenges in providing individual attention to all team members during training. To alleviate the workload of coaches and enhance the effectiveness of self-directed training for team members, this study has developed a cheerleading coach assistant system with artificial intelligence technology. This system demonstrates high judgment accuracy and significantly improves the standardization of basic motions for competitive cheerleaders, reducing the possibility of sports injuries. © The Institution of Engineering & technology 2023.

关键词： Sports

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Leveraging Artificial Intelligence for Enhanced Data Generation in Addressing Imbalance in Binary Classification System

Leveraging Artificial Intelligence for Enhanced Data Generat...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Srijita Bandopadhyay Srimonti Dutta Imran Haider Bhavaraju Anuraag Jerry Zhu Saad Ahmed Bazaz Dept. of Electronics and Communication Engineering University of Engg. & Management Kolkata India School of Agro and Rural Technology Indian Institute of Technology Guwahati Guwahati India Dept. of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. of Electronics and Communication Engineering Vellore Institute of Technology Vellore India Faculty of Mathematics University of Waterloo Ontario Canada Dept. of Computer Science National University of Computer & Emerging Sciences Islamabad Pakistan

This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority Over-sampling Technique (SMOTE), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) to enhance classification outcomes. Traditional classification models tend to favor the majority class, while the impact of imbalanced misclassification costs is often overlooked. The integration of SMOTE, GANs, and VAEs in binary classification, or SMOTE-GAN-VAE, addresses these challenges by generating synthetic instances, refining data representations, and capturing latent features. To evaluate the effectiveness of various data generation methods, a credit card fraud dataset is used. The performance metrics considered include F0.5-score, F1-score, and F2-score, which account for both precision and recall. The results indicate that SMOTE-GAN-VAE outperforms individual methods, such as SMOTE, GANs, and VAEs, demonstrating its potential to enhance data representation and classification accuracy, and outperformed the β- VAE filtered approach employed in previous literature.

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Mingze Li Weihang Ren Lei Fan Yongpei Guan Dept. of Electrical and Computer Engineering University of Houston TX USA Engineering Technology University of Houston TX USA Dept. of Industrial and Systems Engineering University of Florida FL USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation.

关键词： Heuristic algorithms Clustering methods Clustering algorithms Search problems Boundary conditions Power grids Complexity theory Optimization AC power transmission

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Smart Sensor Technologies and Its Location-Aware Applications

Smart Sensor Technologies and Its Location-Aware Application...

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2023 IET International Conference on Engineering Technologies and Applications, ICETA 2023

作者： Wu, Dong-Shing Tu, Jiun-Yu Yang, Chun-Hung Dept. of Computer Science and Information Engineering Southern Taiwan University of Science and Technology No.1 Nantai St. Yungkang Dist. Tainan City710301 Taiwan Dept. of Electronic Engineering Southern Taiwan University of Science and Technology No.1 Nantai St. Yungkang Dist. Tainan City710301 Taiwan

ISBN: (纸本)9781839539848

Owing to the popularity and miniaturization for the embedded systems, many types of sensors are integrated with the embedded systems as the smart sensors by the electronic manufacturers, and thus the uses can apply the estimation capabilities of smart sensors to perform real-time signal processing, judgment, and control function. This paper introduces a unidirectional localization technology with low-complexity computing in smart sensor nodes. The proposed technology formulates the estimated location of a target as an optimization problem based on the real-time signal estimation, and the problem can be solved in the centralized or decentralized way. The computer simulation results illustrate that in a static localization scenario, the suggested location-aware application exhibits superior estimation accuracy compared to the conventional approach. © The Institution of Engineering & technology 2023.

关键词： Smart sensors

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Chatbot for Mutual Fund Assistance

Chatbot for Mutual Fund Assistance

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Shaik Mohammad Waseem Akshai Krishna A Divya Komati Lathika D Noufa Haneefa Batini Dhanwanth Dept. of Computer Science and Engineering Panimalar Institute of technology (Anna university) Chennai India Dept. of AI Engineering Jönköping University Jönköping Sweden

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

A growing number of businesses stand to benefit from chatbots and other virtual assistant bots made possible by advances in artificial intelligence (AI) and machine learning (ML). With a focus on mutual fund investing, this project aims to investigate the potential of contextually-driven chatbots that employ AI and ML approaches to improve user interactions. The purpose of this article is to provide a framework for a chatbot that can automatically gather client data and provide automated, individualized investing advice in the absence of human advisors. To guarantee that customers get individualized responses, the chatbot use domain-specific training models to provide information that is accurate, relevant, and timely. The algorithm helps traders by assessing market movements and providing recommendations for when and which stocks to buy. This study proposes a novel approach to mutual fund investing using conversational AI, which may streamline procedures, increase accessibility, and aid individual investors in making informed decisions. The results show that AI-powered solutions have a lot of promise for providing round-the-clock support, helping users make smart financial decisions with less human interaction.

关键词： Training Accuracy Virtual assistants Mutual funds Machine learning Chatbots Informatics Mobile computing Business

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Video key concept extraction using Convolution Neural Network

Video key concept extraction using Convolution Neural Networ...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Tanvir H Sardar Ruhul Amin Hazarika Bishwajeet Pandey Guru Prasad M S Sk Mahmudul Hassan Radhakrishna Dodmane Hardik Gohel Dept. of Computer Science & Engg School of Technology (GST) GITAM University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru India Department of Intelligent System and Cyber Security Astana IT University Kazakhstan Dept. of Computer Science & Engg. Graphic Era (Deemed to be University) Dehradun India School of Computer Science and Engineering VIT-AP India Department of Computer Science & Engg. NMAM Institute of Technology Nitte India Department of CSE University of Houston Victoria USA

Objectives: This work aims to develop an automated video summarising methodology and timestamping that uses natural language processing (NLP) tools to extract significant video ***: The methodology comprises extracting the audio from the video, splitting it into chunks by the size of the pauses, and transcribing the audio using Google's speech recognition. The transcribed text is tokenised to create a summary, sentence and word frequencies are calculated, and the most relevant sentences are selected. The summary quality is assessed using ROUGE criteria, and the most important keywords are extracted from the transcript using ***: Our proposed method successfully extracts key points from video lectures and creates text summaries. Timestamping these key points provides valuable context and facilitates navigation within the lecture. Our method combines video-to-text conversion and text summarisation with timestamping key concepts, offering a novel approach to video lecture analysis. Existing video analysis methods focus on keyword extraction or summarisation, while our method offers a more comprehensive approach. Our timestamped key points provide a unique feature compared to other methods. Our method enhances existing video reports by (i) providing concise summaries of key concepts and (ii) enabling quick access to specific information through timestamps. (iii) Facilitating information retrieval through a searchable index. Further research directions: (i) Improve the accuracy of the multi-stage processing pipeline. (ii) Develop techniques to handle diverse accents and pronunciations. (iii) Explore applications of the proposed method to other video genres and ***/Improvements: This approach is practical in giving accurate video summaries, saving viewers time and effort when comprehending the main concepts presented in a video.

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Twitter Data Analysis using Natural Language Processing and Recurrent Neural Network 15

Twitter Data Analysis using Natural Language Processing and ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Deshmukh, Sachin Ali, M.S. Bhagat, Amol P. Kakde, Vikram Dept. of Computer Science and Engineering Prof Ram Meghe College of Engg. & Mgmt. Amravati Badnera India Dept. of Information Technology Prof Ram Meghe College of Engg. & Mgmt. Amravati Badnera India Department of Electronics and Telecommunication Engineering Prof Ram Meghe College of Engg. & Mgmt. Amravati Badnera India

ISBN: (纸本)9798331300579

The essence of this work presented is the conception and construction of a unique model. This model harnesses the power of Natural Language Processing (NLP) and Recurrent Neural Networks (RNN) to decipher the sentiments expressed across various social media platforms. The ultimate goal is to illuminate the emotional landscape of the digital world. In the work presented in this paper the twitter social media posts/messages are retrieved having a particular keyword. Many posts are made using identical message formats which users are unaware about. For effective retrieving of the post identifying the relevant information is required. Many social media post have data which are irrelevant to analyze. For effective analysis removing this irrelevant data is necessary. Once the posts are processed, tokens and keywords are identified. Keywords are important aspects which are used further to identify the sentiment. The processed data is then stored in database and split into two category for training and testing purpose. The polarity and subjectivity of the data is then calculated for analyzing the sentiments using NLP and RNN. The proposed NLP-RNN approach is evaluated using accuracy, loss, and evaluation time required. © Grenze Scientific Society, 2024.

关键词： Tweets

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Demystifying Existing Sentiment Analysis Approaches of Hindi and English Languages using Machine Learning

Demystifying Existing Sentiment Analysis Approaches of Hindi...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Hari Om Avinash Kumar Sharma Dept. of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Gr Noida Uttar Pradesh India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The number of operational information acquired, such as product reviews in several dialects, has expanded as the digital era has progressed. Many businesses, such as online service providers, benefit greatly from information analysis. Sentiment analysis (SA) is the technique of deciphering & categorizing the opinions/viewpoints/sentiments following the content (assessment) of user via text analysis tool (SA). Sentiment Analysis is a Natural Language Processing (NLP) program that is exercised in order to determine opinions of online consumers' comments, evaluations, thoughts, viewpoints and so on. Nowadays, social networking websites like Facebook & Twitter are recurrently utilised to submit consumer assessments regarding a variety of topics like politics, news, movies, fashion, food, etc. Viewpoints, Opinions & Reviews are important in determining the degree of gratification of consumers with a given unit. To determine the polarity, which might be neutral, negative, or positive. This study discusses a Sentiment Analysis technique on different social media platform reviews in Hindi. This paper presents a comparative study of sentiment analysis on Hindi and English texts using machine learning techniques. Datasets are accumulated of Hindi & English texts from various online sources, including social media platforms, news websites, and movie review sites. Various machine learning algorithms, including Naive Bayes, Support Vector Machines, Random Forest, and Logistic Regression, to perform sentiment analysis on the datasets.

关键词： Support vector machines Sentiment analysis Logistic regression Text analysis Machine learning algorithms Social networking (online) Reviews Motion pictures Random forests Information analysis

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