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Resilient Energy Hub Management: A Consensus-Based Approach for Lossy Communication Networks 25

Resilient Energy Hub Management: A Consensus-Based Approach ...

引用

25th International Middle East Power system Conference, MEPCON 2024

作者： El-Afifi, Magda I. Osman, Samaa F. Sedhom, Bishoy E. Eladl, Abdelfattah A. Nile Higher Institute for Eng. and Technology Dept. of Elect. and Comm. Eng. Mansoura Egypt Mansoura University Faculty of Engineering Electrical Engineering Department El-Mansoura35516 Egypt

ISBN: (纸本)9798350379648

A distributed consensus strategy is presented in this paper to address the energy hub (EH) dispatch problem in the context of packet loss and EH uncertainty. This approach was motivated by the difficulty of concentrated energy management approaches and massive amounts of data that were exchanged, which also included a problem with packet loss. Each unit of generation and consumption is responsible for computing power generation, load, and mismatches, and the coordination of energy nodes is controlled in a decentralized manner. Workload sharing, robustness, effective decision-making, rapid convergence, and node autonomy are some of the benefits that can be gained from utilizing this distributed technique. The basic objective, which is applicable to both connected and isolated grids, is to accomplish the reduction of operational costs and emissions while simultaneously satisfying demand and operating within system restrictions. Significant gains are demonstrated by two case studies with the presences packet loss and without packet loss. These studies show that there is a reduction of power mismatch by one hundred percent for the electricity grid, 99.96 percent for heating, and 99.95% for gas. A further decrease of 13.27% was seen in overall operating expenses, while a 30.37% reduction was seen in total emissions. © 2024 IEEE.

关键词： Packet loss

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Maximise Yield of Crop using Recommendation system based on Machine learning

Maximise Yield of Crop using Recommendation system based on ...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Sharma, Lakshya Agarwal, Shikha Singhal, Devansh Gupta, Ayush Chaudhary, Aarti Ajay Kumar Garg Engineering College Department of Information Technology U.P. Ghaziabad India Ajay Kumar Garg Engineering College Dept. Name of Organization U.P. Ghaziabad India

ISBN: (纸本)9798350382723

Data mining is the way of interrogate and concluding decisive information from raw data. Data Mining discovers its usage in various areas like agriculture, health, retail, finance, medicine and etc. Data mining is used in agriculture for establishing the correlation between soil nutrients and climate for determining the farmer's decision making. Crop farming is dependent on soil nutrients like phosphorus, calcium, and potassium as well as soil PH, temperature, and rainfall. These factors are investigated using a variety of techniques, including Random Forest, ANN, SVM, NB, LR, and DT algorithms. Our research paper's objective is to identify the ideal crop type. Our farmers are better able to choose the crop that is best suited for the soil in their fields as a result of our efforts to try and solve one of their problems [26]. This paper also covers the theoretical and conceptual aspects of crop recommendation systems using a concept of machine learning and its various techniques. We also had to make sure that our model was more accurate and efficient than others and that it was updated regularly to ensure that users received updates on time. We need to gather data from a particular area that includes all relevant factors, such as the soil and environment. With 99.77% accuracy for crop prediction, Random Forest is the best algorithm among all the techniques used in our model. The random forest algorithm makes use of decision tree features in addition to its own. © 2024 IEEE.

关键词： Crops

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Adaptive Fuzzy Predictor Based Fast Terminal Sliding Mode Controller Design for Two-Link Robot Manipulator

Adaptive Fuzzy Predictor Based Fast Terminal Sliding Mode Co...

引用

Indian Control Conference (ICC)

作者： Shreyas Kumar Bishesha Dash Roshni Maiti Dipayan Guha Dept. of Mechanical Engineering Indian Institute of Engineering Science and Technology Shibpur Dept. of Electrical Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj

ISBN: (数字)9798331517212

ISBN: (纸本)9798331517229

This paper proposes an Adaptive Fuzzy Predictor based Fast Terminal Sliding Mode Control (AFP-FTSMC) for a two-link robotic manipulator (TLRM). The unknown disturbances, changing with time, present in the TLRM system are estimated using the fuzzy predictor model and the accurate control action is formulated using the FTSM controller to tackle the nonlinearities in the system. The disturbance, varying with time, is estimated using adaptive fuzzy predictor, and the fuzzy adaptive rule is developed from the Lyapunov stability criteria. The chattering phenomenon and singularity problem is efficiently addressed by utilizing the fast terminal sliding mode control law. The overall controller is capable of producing fast-tracking performance by rejecting the disturbance and chattering adequately. The stable performance of the TLRM system with the proposed controller is guaranteed by utilizing Lyapunov stability criteria. The experimental results demonstrate that the proposed controller achieves superior tracking performance and robustness for nonlinear TLRM system compared to the existing terminal sliding mode control method, even in the presence of significant uncertainties and time-varying external disturbances.

关键词： Adaptive systems Uncertainty Predictive models Manipulators Robustness Energy efficiency Nonlinear systems Time-varying systems Sliding mode control Lyapunov methods

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Optimizing Road Extraction in Remote Sensing Imagery: A Lightweight Dilated Inception Deep Residual U-Net with Channel Attention (LDIDRU-CAN) Model and Pruning Strategies 15

Optimizing Road Extraction in Remote Sensing Imagery: A Ligh...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Palvi Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Dept. of Computer Science & Engineering Punjab India University of Kufa Faculty of Computer Science & Mathematics Najaf Iraq National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350370249

The major challenge in Road Extraction is to extract accurate and complete road network and to get high accuracy while minimizing computational power and ensuring faster prediction. It plays important role in urban planning, disaster management, automatic vehicle navigation and GPS navigation. Several earlier studies used complex deep networks that required large amounts of computational power and large storage. In order to address issues such as high storage requirements, computational load, access to memory delays, and low hardware deployment efficiency this research work presents a novel network model called LDIDRU-CAN. The proposed model is fast, compact, and efficient and requires less storage. The objective of this work, is to compress the original model using structured pruning strategy. This strategy simplified the original model and reduced their computation cost as well as storage requirements. For the reduction of the parameters, we focused on the Row Scale Regular Pruning (RSRP) and Low Learning Rate (LLR) retraining. After applying RSRP pruning approach it reduces storage requirements, parameters, and also contributes to a more than 50% reduction in hardware resource consumption and successfully enhances the parallelism of the model. Testing and validations are performed on the Massachusetts Road Dataset. With only 45 M parameters and 180 MB model size, the suggested network achieves remarkable performance with 87.91% precision, 85.51% recall, 86.69% F1-score and 98.91% accuracy. © 2024 IEEE.

关键词： Channel Attention LLR Pruning Road Extraction RSI RSRP

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Online Signature Recognition using Transformer Networks: An Overview 6

Online Signature Recognition using Transformer Networks: An ...

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6th IEEE International Conference on Advances in Science and technology, ICAST 2023

作者： Bharadi, Vinayak Hamdule, Zohaib Kambli, Sujan Salvi, Raj Chavan, Raj Nimbalkar, Vrishali Finolex Academy of Management and Technology Department of Information Technology Ratnagiri India Finolex Academy of Management and Technology Dept. of Computer Science and Engineering Ratnagiri India

ISBN: (纸本)9798350359817

Online signature verification remains a pivotal component in digital security, ensuring the authenticity of digital transactions and documents. This paper offers a comprehensive overview of prevailing online signature verification techniques, with a particular emphasis on methods employing Deep Learning. RNNs, with their inherent capability to process sequential data, have been instrumental in improving the accuracy and efficiency of signature verification systems. However, as the digital forgery landscape becomes increasingly sophisticated, there is a pressing need to explore advanced neural architectures. In this context, the paper introduces a transformer-based approach for signature verification. Unlike traditional models, transformers, with their self-attention mechanisms, have the potential to capture intricate signature patterns and nuances, offering a promising avenue for future research and application. © 2023 IEEE.

关键词： Recurrent neural networks

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Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography

Comparative Study of Face Tracking Algorithms for Remote Pho...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Jayasinghe, J.A.S.Y. Katsigiannis, Stamos Malasinghe, Lakmini Sri Lanka Institute of Information Technology Dept. of Electrical and Electronic Engineering Malabe Sri Lanka Durham University Dept. of Computer Science Durham United Kingdom

ISBN: (纸本)9798350327816

Remote Photoplethysmography (rPPG) is a non-invasive approach for monitoring Heart Rate (HR) that can be used in various applications in healthcare and biometrics. rPPG measurements acquired using facial videos have become very popular and one of the main steps of this technique is facial tracking and Region of Interest (ROI) extraction. This research paper investigates four widely used face tracking algorithms, namely MediaPipe Face Mesh (MPFM), Haar Cascade, Multi-task Cascaded Convolutional Network (MTCNN), and Dlib, concerning their ROI extraction capabilities for rPPG HR measurements. Using evaluation metrics such as accuracy, processing time, and ease of extracting ROIs, this work also recommends the most suitable face tracking algorithm from those mentioned above for rPPG measurements, and presents a compilation of a prioritization list of ROIs based on their sensitivities for rPPG measurements. Experimental results showed that the MPFM algorithm and cheek ROIs provided the best HR measurements. © 2023 IEEE.

关键词： Face Tracking Machine Learning Remote Photoplethysmography ROI Extraction

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Application of Blockchain technology for Power system Security using Condition Number 2

Application of Blockchain Technology for Power System Securi...

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2nd International Conference on Trends in Electrical, Electronics and Computer engineering, TEECCON 2023

作者： Daram, Suresh Babu Golla, Namratha Angadi, Ravi V Bandam, Rahul Vijayalakshmi, K.A. Sesiprabha, Killada Mohan Babu University Department of Electrical and Electronics Engineering Tirupati A.P India Sree Vidyanikethan Engineering College Department of Electrical and Electronics Engineering Tirupati A.P India Presidency University Dept. of Electrical and Electronics Engineering Karnataka Bengaluru India Sir M v Institute of Technology Dept. of Electrical and Electronics Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350339949

Block chain technology secures electricity generation, transmission, and distribution. Contingency analysis helps assess and improve power system security. Contingency analysis predicts transmission line failure, equipment failure, and other disturbances to ensure energy system reliability. The power system security study, including assessment and augmentation, will use a lot of data from contingency analysis. Contingency analysis predicts transmission line failure, equipment failure, and other disturbances to ensure energy system reliability. The system's operation and maintenance depend on contingency analysis data. To make good decisions, public officials require these facts. This article proposes using blackchain technology for contingency analysis and data sharing. By modifying gearbox system characteristics, the condition number will improve power system controllability, stability, and transfer. Blockchain can protect power system data and ensure an uninterrupted power supply. The Python programmed developed and MATLAB software is used to generate the data under contingency condition. In this case UPSEB 75 bus system is considered for the study. © 2023 IEEE.

关键词： Blockchain

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Personality Traits Assessment in the Learning Process Using Note-Taking and system Operation Behavior - A Case Study of a Front-End Web Design Course 3

Personality Traits Assessment in the Learning Process Using ...

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3rd IEEE International Conference on Social Sciences and Intelligence Management, SSIM 2023

作者： Lin, Jia-Rou Tseng, Chun-Hsiung Lin, Hao-Chiang Koong Huang, Andrew Chih-Wei YuanZe Univ Dept. of Electrical Engineering Taoyuan Taiwan National Univ. of Tainan Dept. of Information and Learning Technology Tainan Taiwan Fo Guang Univ Dept. of Psychology Yilan Taiwan

ISBN: (纸本)9798350327786

We analyzed personality traits using the user behavior information of college students in programming courses. Students' personality traits were understood by utilizing readily available information from the teaching process in the classroom. Suitable learning strategies were recommended for students based on the results of this analysis. We employed a self-developed system for testing to collect students' notes and record their operational behaviors in the practicing process. These data were then used for subsequent analysis focusing on the transitions between note-taking and programming states. 45 participants participated in this study. Personality traits such as conscientiousness, and openness to experience were used to classify the participants. Previous studies indicated a higher correlation between conscientiousness, openness to experience, and learning strategies. With a two-layer random forest model, an error rate of 10.71% was achieved for the developed first-layer model to classify participants based on their note content, conscientiousness, and openness to experience. The second-layer model used note content and operational behavior and openness to experience. © 2023 IEEE.

关键词： Students

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Impersonated Human Speech Chatbot with Adaptive Frequency Spectrum 3

Impersonated Human Speech Chatbot with Adaptive Frequency Sp...

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3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Chettiar, Gautam Shukla, Amogh Nalwaya, Preet Sethi, Karman Prakash, Surya Vellore Institute of Technology Dept. of Communication Engineering Vellore India Department of Software Systems Vellore Institute of Technology Vellore India Indian Institute of Information Technology Allahabad Department of Electronics and Communication Engineering Prayagraj India

ISBN: (纸本)9781665453578

Recent trends in artificial intelligence and natural language processing models have led to the generation of highly efficient and versatile intelligent chatbot models., which have the potential to supplant human speech to the level of conversational proficiency. The proposed method creates a chatbot model that trains itself on open conversation datasets and aims to impersonate without compromising the emotional sentiments in the voice. These datasets extract from the applications such as WhatsApp., Telegram., Messenger., or any other chatting platform. Datasets convert to a machine-readable format., which is dynamically updated in real-time during the conversation., and then using speech conversion algorithms convert the reply into the desired individual's voice. The proposed model's conversational ability depends on the amount of conversation data., which gives the output in the person's voice frequency. By using an NLP-based chatbot trained on personalized data using KNN., and handling misses by pipelining the chatbot inputs to the GPT-2 model., the model can generate human-like replies even if there is data insufficiency. The natural replies are complemented with matching human voice and tone characteristics by using the vocoder model., which matches the spectral characteristics of the target voice onto the required voice. This opens a plethora of commercial and therapeutic applications that provide excellent insights into implementing natural communication models for humanoid and robotics innovations. © 2023 IEEE.

关键词： Sentiment analysis

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An Explainable Approach for Early Diagnosis of Parkinson's Disease Exploring Motor Symptoms 3

An Explainable Approach for Early Diagnosis of Parkinson's D...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Aversano, Lerina Madau, Antonella Montano, Debora University of Foggia Department of Agricultural Food Natural Resources and Engineering Sciences Foggia Italy University of Sannio Dept. of Engineering Benevento Italy Regional Center Information Communication Technology CeRICT scrl Benevento Italy

ISBN: (纸本)9798350378009

Parkinson's disease is a progressive neurodegen-erative disease that affects the nervous system causing severe cognitive, motor, and behavioral dysfunctions. It is mainly caused by a progressive loss of neurons in the substantia nigra in the basal ganglia and this leads to abnormalities in the control of movements. The symptoms progress slowly over the years and for this reason, the diagnosis of Parkinson's disease is very complex, so it is important to support medical personnel in identifying and predicting the disease with Artificial Intelligence tools and techniques. This study proposes a new approach to predict disease onset and monitor patient progress through Explainable Artificial Intelligence (XAI) that aims to provide clear and understandable explanations for decisions made by machine learning algorithms. Initially, different classification algorithms were evaluated and, by choosing the one with the best performance, the most significant features were identified through the use of SHAP. The model was subsequently re-evaluated with only the selected features, evaluating the use of the XAI for disease identification. The methodology was validated on the Parkinson's Progression Markers Initiative (PPMI), more specifically taking into consideration only the characteristics that concern the motor sphere. © 2024 IEEE.

关键词： Neurodegenerative diseases

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