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International Symposium on Industrial Electronics (INDEL)

作者： Emelie Berglund Johan Borg Jonny Johansson Dept. of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

ISBN: (数字)9798350352320

ISBN: (纸本)9798350352337

The use of inverter-based motor controllers that can generate fast voltage level transitions is increasing in the field of electric drive systems because of their ability to achieve higher efficiency. However, the fast voltage transitions, dv/dt's, are prone to generate bearing currents and cause overvoltages that may harm the electric motor. An LC filter applied between the inverter and the motor terminals can reduce the dv/dt's. However, the LC filter will cause overvoltage due to resonance but this can be overcome by introducing an extra pulse at each pulse-width modulation (PWM) transition. The timing of this extra pulse is crucial for suppressing the resonance, but due to nonlinearities in a real system, the time instants may vary over time and are thus in need of control. In this study, an analytical expression for the optimal pulse location and duration is derived and we propose a control algorithm that continuously calculates the optimal pulse switch time instants based on the measured output voltage. The control algorithm can handle sudden system disturbances and a Monte Carlo simulation is performed to evaluate the performance of the algorithm. This hybrid filter successfully reduces dv/dt's at the motor terminals while simultaneously eliminating overvoltages due to the filter resonance.

关键词： Voltage measurement Monte Carlo methods Heuristic algorithms Filtering algorithms Pulse width modulation Resonance Inverters Timing Surges Voltage control

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Fair and Efficient Congestion Management for Low Voltage Distribution Networks

Fair and Efficient Congestion Management for Low Voltage Dis...

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IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Aswin Ramesh Vadavathi Gerwin Hoogsteen Johann Hurink dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede the Netherlands

The widespread increase of photovoltaic panels in the distribution grid often leads to overvoltage. Traditional voltage regulation techniques are unfair for prosumers at the sensitive part of the grid due to larger power curtailment. Consequently, there has been considerable research on implementing fair curtailment strategies for congestion management. However, existing fairness-incorporated curtailment approaches yield varying results based on changes in the grid topology and integration of renewable energy sources. Therefore, this paper introduces proportional voltage fairness that is independent of the above-mentioned network properties and inherently distinguishes regions for fair curtailment. Additionally, there is a need to consider the underutilisation of the grid while adhering to a fair curtailment scheme. Thus this paper proposes a control algorithm incorporating proportional voltage fairness and gives the opportunity to specify the trade-off between fairness and the total power curtailed. Further, a comparative analysis with respect to state-of-the-art notions of fairness is performed.

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Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble and Hybrid CNN-LSTM Models

Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Maharin Afroj Khan Md Sadbin Rifat Md. Saifur Rahman Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Software-Defined Networking (SDN) faces significant challenges in mitigating Attacks known as denial of service (DoS) and distributed DDoS due to its centralized control plane architecture. This centralization makes the control plane an attractive target for attackers, highlighting the need for effective detection and mitigation strategies. In this study, we propose three innovative methodologies to enhance the accuracy of detecting DoS/DDoS attacks in SDN environments. Our first methodology employs an ensemble model with a voting classifier, which achieves a detection accuracy of 97.4%. This approach leverages multiple weak learners to enhance the robustness of detection. The second methodology utilizes an ensemble with a stacking technique, attaining a superior accuracy of 99%, demonstrating the efficacy of integrating multiple base models in a hierarchical fashion. The third approach introduces a novel hybrid model that blends LSTM networks with CNN, achieving a competitive accuracy of 98.51%. A comparative analysis of these methods reveals that, while all three significantly enhance detection rates, the stacking ensemble technique outperforms the others. This finding underscores the potential of advanced ensemble techniques in effectively addressing the dynamic and evolving cyber threats within SDN environments. Our study offers valuable insights into strengthening SDN infrastructures against DoS/DDoS attacks, contributing to the ongoing efforts to secure modern network architectures.

关键词： Logistic regression Accuracy Prevention and mitigation Stacking Network architecture Robustness Real-time systems Ensemble learning Software defined networking Long short term memory

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Applications and Approaches of Sentiment Analysis: A Current Review

Applications and Approaches of Sentiment Analysis: A Current...

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IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Sandra G Salaja Silas Elijah Blessing Rajsingh Dept. of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

Limitless amount of data is loaded in every moment in social media platforms in the forms of audio, videos, texts, and photos. They are rich sources of information, which has immense value. Hence extraction of information from them is inevitable. Many methods are employed for this and the popular among them to mine the information is sentiment analysis. To materialize the objectives of this study, a systematic review of articles published from 2013 to 2023 was done. Our study reviewed the relevance of sentiment analysis in social media by probing the different approaches followed, the platforms used and its applications, as its main objectives. Its results show that majority of studies applied machine learning approach or hybrid method for executing sentiment analysis.

关键词： Sentiment analysis Social networking (online) Voting Decision making Machine learning Disaster management Fraud Stakeholders Forecasting Meteorology

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Nonparametric Estimation of Local Bandwidth from Level Crossings Sampling

Nonparametric Estimation of Local Bandwidth from Level Cross...

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2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Pawlak, Miroslaw Rzepka, Dominik Miskowicz, Marek Agh University of Science and Technology Dept. of Measurement and Electronics Krakow Poland University of Manitoba Canada Dept. of Electrical and Computer Engineering WinnipegR3T 5V6 Canada

ISBN: (纸本)9798350328851

We study the problem of a local bandwidth recovery for nonstationary stochastic signals when the measured information is given in terms of level crossings. We propose a kernel estimate of the local bandwidth from samples generated from level crossings of stochastic signals being the time-warped version of stationary Gaussian processes. The positivity and bandlimited nature of the local intensity is captured by the properly selected class of bandlimited and positive kernel functions. The asymptotic properties of the estimator are derived. © 2023 IEEE.

关键词： Railroad crossings

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Modeling Neural Circuit of Lower Urinary Tract

Modeling Neural Circuit of Lower Urinary Tract

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Hunter Bushnell Isabel Banks Vinay Guntu Gregory Glickert Satish S. Nair Dept. of Electrical Engineering & Computer Science University of Missouri Columbia MO USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

We report a biophysical network model of the mammalian lower urinary tract including the neural circuit that controls its closed loop function. The model was able to reproduce the firing rates and bladder dynamics during a normal fill and void cycle. We were then able to adapt the model to produce the changes and effects of spinal cord injury, which causes severe dysfunction in micturition function and control.

关键词： Adaptation models Firing Circuits and systems Biological system modeling Neural circuits Bladder Spinal cord injury

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Breaking Barriers: Real-Time Sign Language Recognition Using LSTM Networks for Enhanced Communication Accessibility

Breaking Barriers: Real-Time Sign Language Recognition Using...

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International Conference on Advanced Systems and Electric Technologies (IC_ASET)

作者： Halah Magri Mohamed Tarik Moutacalli dept. of Mathematics Computer Science and Engineering University of Quebec at Rimouski Rimouski Canada

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

This paper presents an innovative approach to real-time sign language recognition using Long Short-Term Memory networks (LSTM), aimed at enhancing communication accessibil-ity for the deaf and hard-of-hearing community. We address the challenge of understanding and interpreting sign language, which is critical for millions worldwide, yet restricted to those proficient in it. Our research contributes to bridging this communication gap by developing a deep learning model capable of recognizing a broad spectrum of sign language gestures and sentences with high accuracy and speed. Utilizing a rich dataset comprising diverse sign language gestures, collected in collaboration with a professional video production studio and proficient sign language users, we employ LSTM networks integrated with Dense layers to effectively capture the complex spatial and temporal patterns of sign language. The architecture of our model is specifically designed to accommodate the nuanced dynamics of sign language, with an emphasis on real-time processing. Through rigorous training and validation, our model demonstrates an outstanding accuracy rate of 92 % on a comprehensive testing dataset, alongside remarkable real-time processing capabilities. The sys-tem's efficiency in recognizing a wide array of sign gestures nearly instantaneously underscores its potential applicability in various real-world scenarios, including assistive technologies and human-computer interaction. This study not only showcases the practicality and efficacy of LSTM networks in real-time sign language recognition but also marks a significant step towards more inclusive and accessible communication technologies. Our future work includes integrating this system with the Langue des Signes Quebecoise website, further advancing the goal of universal communication accessibility.

关键词： Training Human computer interaction Sign language Accuracy Production Streaming media Real-time systems

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Improving Time Series Forecasting Accuracy with Transformers: A Comprehensive Analysis with Explainability

Improving Time Series Forecasting Accuracy with Transformers...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Susmera Joseph Ashly Ann Jo Ebin Deni Raj Dept. of Computer Science Engineering Indian Institute of Information Technology Kottayam India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Time series forecasting is crucial in numerous sectors, including healthcare, energy, and finance. Transformer models, initially designed for natural language processing applications, demonstrate potential in capturing temporal dependencies within sequential data. In this paper, the application of transformer models in time-series forecasting is explored, and their performance and interpretability are investigated. Transparent models build trust and facilitate decision-making, especially in industries with ethical guidelines. While inherently interpretable time-series Transformers exist, there is a gap in research regarding post hoc methods, which is an area ripe for exploration. Understanding and comparing complex Transformer models for time series forecasting can benefit from advancements in explainability, ensuring accurate and comprehensible predictions. In comparison to five other transformer-based models and classical models, the Temporal Fusion Transformer (TFT) model outperformed them in short-term forecasting. Further enhancement of the model performance was achieved through a meticulous analysis of the Encoder-Decoder’s importance, which contributed significantly to refining the forecasting accuracy. The vanilla transformer was explained using permutation feature importance. The model stability was analyzed using perturbation methods.

关键词： Analytical models Accuracy Perturbation methods Time series analysis Refining Predictive models Transformers Stability analysis Thin film transistors Forecasting

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Smart Healthcare - IoT and Artificial Neural Network(ANN)

Smart Healthcare - IoT and Artificial Neural Network(ANN)

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International Conference on Computing and Networking Technology (ICCNT)

作者： Laxmikanta Sutar Suchismita Chinara Dept. of Computer Science and Engineering National Institute of Technology Rourkela Rourkela India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

With the use of the Internet of Medical Things (IoMT), the growing accessibility of wearable devices, and the continual reduction in the cost of sensor-based technologies and green computing, internet-connected sensors allow users to track, analyze, and share welfare information employing a cloud-based platform. Artificial Neural Networks (ANN) find extensive use across various applications, including healthcare systems, drawing inspiration from the biological concept of neurons. This study explores the prediction of heart attack risks using a combination of Internet of Things (IoT) sensors and artificial neural networks (ANN). Through the integration of IoT sensors such as the pulse sensor and LM35 temperature sensor, essential physiological data related to heart rate and body temperature was collected. An ANN model was developed, focusing on predictive capabilities using the sigmoid function, feedforward and backpropagation techniques, and random weight initialization. The investigation further utilized Keras with TensorFlow and optimized model parameters using Adam Optimization and Later with One Hot Encoding plus a Genetic Algorithm (GA). Additionally, the study involved training neural networks on higher-quality UCI datasets to evaluate performance. Scatterplots were generated to visualize relationships between variables. Results demonstrated promising performance improvements, with the model achieving close to ${9 0 \%}$ accuracy on the testing dataset. Encoding techniques were employed to handle categorical variables effectively, contributing to the model’s robustness and generalization. Overall, this research offers valuable insights into leveraging IoT sensors and ANN for heart attack risk prediction.

关键词： Temperature sensors Training Artificial neural networks Cardiac arrest Encoding Sensors Biological neural networks Optimization Intelligent sensors Genetic algorithms

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Joint Energy and Computation Workload Management for Geo-distributed data Centers

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Wang, Ran Wu, Rixin Liu, Linfeng Yi, Changyan Zhu, Kun Wang, Ping Niyato, Dusit Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing China York University Dept. of Electrical Engineering and Computer Science Lassonde School of Engineering Canada Nanyang Technological University School of Computer Science and Engineering Singapore

The increasing demands of data computation and storage for cloud-based services motivate the development and deployment of large-scale data centers (DCs). The energy demand of these devices is rising rapidly and becoming a noticeable challenge for current power networks. The smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the load pressure from DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, which can benefit SGs and DCs, as well as society at large. However, the geo-distributed property of DCs and SGs and the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such a tripartite coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, a three-layer game is formulated to model the iterations among entities including the energy market, data center operators (DCOs), and SGs. The market includes a certain amount of RES that must be dispatched. The SG offers the DCO an electricity selling price while simultaneously importing RES from the market at a buying price in order to maximize the benefit. The DCO allocates the workload to different DCs, aiming to minimize the costs of energy consumption and carbon emissions. The interactive processes between different entities are further decomposed into two coupling Stackelberg games. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the joint energy and computation workload management scheme and show its superiority over counterparts in utilizing renewable energy and reducing emissions. Furthe

关键词： Smart power grids

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