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Edge computing and Computational Task Offloading Analysis - A review study

Edge Computing and Computational Task Offloading Analysis - ...

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2022 international conference on Emerging Trends in electrical, control, and Telecommunication engineering, ETECTE 2022

作者： Zaidi, Tanzeel Aziz, Samia Usman, Muhammad Azam, Awais Cheema, Adeel Ashraf Ajmal, Sahar National University of Computer and Emerging Sciences Department of Computer Science Faisalabad Chiniot Pakistan

ISBN: (纸本)9781665492423

Delay constraints and energy consumption are becoming a barrier to running complex applications on mobile devices due to the rise of latency-sensitive applications. One of the fundamental technologies of Mobile Edge computing (MEC), which compensates for the limitations of mobile devices in terms of storage space, computing power, and battery efficiency, is computation offloading. The computation offloading methods in MEC networks are now the subject of extensive study for both the industry and academia, using a variety of valuable techniques and methodologies. This paper provides a detailed analysis of computing task offloading in a MEC environment. This study focuses on essential challenges related to numerous offloading goals, such as delay time, energy consumption reduction, income maximization, and system utility enhancement. © 2022 IEEE.

关键词： Computation offloading

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Wireless Sensor Network using control Communication and Monitoring of Smart Grid 2

Wireless Sensor Network using Control Communication and Moni...

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2nd international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2022

作者： Kumar, T. C. H. Anil Dixit, Ganesh Kumar Singh, Rajesh Narukullapati, Bharath Kumar Chakravarthi, M. Kalyan Gangodkar, Durgaprasad Vignan's Foundation for Science Technology and Research Department of Mechanical Engineering Andhra Pradesh Guntur Dt. India Department of Engg. & Tech MP Bhopal India Uttaranchal University Uttaranchal Institute of Technology India Vignan's Foundation for Science Technology and Research Department of Electrical and Electronics Engineering Guntur India VIT-AP University School of Electronics Engineering Amaravathi India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9781665437899

For some countries around the world, meeting demand is a serious concern. Power supply market is increasingly increasing, posing a big challenge for various countries throughout the world. The increasing expansion in the market for power needs upgrading system dependability to increase the smart grid's resilience. This smart electric grid has a sensor that analyses grid power availability and sends regular updates to the organisation. The internet is currently being utilized to monitor processes and place orders for running variables from faraway places. A large number of scanners have been used to activate electrical equipment for domestic robotics for a long period in the last several days. Conversely, if it is not correctly implemented, it will have a negative impact on cost-effectiveness as well as productivity. For something like a long time, home automation has relied on a large number of sensor nodes to control electrical equipment. Since there are so many detectors, this isn't cost-effective. In this article, develop and accept a wireless communication component and a management system suitable for managing independent efficient network units from voltage rises and voltage control technologies in simultaneous analyzing system reliability in this study. This research paper has considered secondary method to collect relevant and in-depth data related to the wireless sensor network and its usage in smart grid monitoring. © 2022 IEEE.

关键词： Internet of things

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Dual-Population-Iterating and Preys-Gathering MPA for XGBoost Optimization 16th

Dual-Population-Iterating and Preys-Gathering MPA for XGBoos...

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16th international conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Li, Jiacheng Lv, Li Kang, Ping Tian, Xiumei School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City Nanchang Institute of Technology Nanchang330099 China

ISBN: (纸本)9789819615308

The marine predator algorithm (MPA) often struggles with issues such as low population diversity, susceptibility to local optima, and population aggregation when optimizing XGBoost, which is sensitive to numerous parameters. To address these challenges, we propose the dual-population-iterating and prey-gathering marine predator algorithm (DPGMPA) and apply it to XGBoost parameter optimization. Initially, we utilize a dual-population-iterating strategy to create the original population and an opposition-based learning population. Brownian motion and Laplace distribution respectively control the step size to guide population updates toward the global optimum, enhancing population diversity. Subsequently, the preys-gathering strategy is employed, selecting prey with the highest accuracy in each iteration and having neighboring preys converge towards them to mitigate local optima. Furthermore, we optimize the Fish Aggregating Devices (FADs) by introducing variable weight control, addressing the population aggregation issue. To validate the parameter optimization capability of this algorithm for XGBoost, we conducted experiments on six datasets, using accuracy and F1 score as evaluation metrics, and compared the results with five other algorithms. The experimental results demonstrate that the DPGMPA algorithm more effectively escapes local optima when optimizing the XGBoost classifier, significantly improving XGBoost's classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Laplace distribution Marine Predator Algorithm normal distribution parameter optimization population diversity XGBoost

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Development of Thyristor controlled DC electrical Drive Test Bench with Virtual Inertia Emulation 63

Development of Thyristor Controlled DC Electrical Drive Test...

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IEEE 63rd international Scientific conference on Power and electrical engineering of Riga-Technical-University (RTUCON)

作者： Gaspersons, Kristians Kroics, Kaspars Riga Tech Univ Inst Ind Elect & Elect Engn Riga Latvia

ISBN: (数字)9781665465588

ISBN: (纸本)9781665465588

This paper describes development of DC motor test bench that is based on thyristor power electronics converters and allows multiple type load tests for scientific and education purposes. The control of the test bench is automated thus allowing create multiple load profiles, automatic data acquisition that makes it more attractive for student's laboratory practice. The digital twin of the test bench has been created into Matlab Simulink software. Using this model it is possible analyze stability of the speed regulator theoretically and design stable PID regulator and after that provide experimental result validation by using test bench. The paper will discuss control algorithms that are used in the motor control and motor parameter experimental detection. There is implemented also possibility virtually emulate different values of inertia thus showing influence of different inertias on speed regulation process.

关键词： electrical drives dynamometer test bench digital twin inertia emulation.

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Prediction the Number of Lamps Required for the Lighting System According to the JKR Lux Standards by Using the Artificial Neural Network Method 13

Prediction the Number of Lamps Required for the Lighting Sys...

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13th IEEE international conference on control System, computing and engineering, ICCSCE 2023

作者： Zulkifli, Nuzul Lokmanhakim Idin, Mohamad Adha Mohamad Muhamad, Samshul Munir Samat, Ahmad Asri Abd Ahmad, Khairul Azman Napiah, Noorezal Atfyinna Mohd Universiti Teknologi Mara Cawangan Pulau Pinang Centre for Electrical Engineering Studies Permatang Pauh Malaysia Universiti Teknologi Mara Cawangan Pulau Pinang Faculty Applied Science Permatang Pauh Malaysia

ISBN: (纸本)9798350323184

This paper aims to develop an advanced neural network-based model for accurately predicting the number of lamps required in lighting system designs, with a focus on cost-effectiveness and compliance with JKR Standards. The scope of the research encompasses the design and evaluation of the model using a comprehensive dataset and a range of parameters. The methodology involves leveraging machine learning techniques, specifically neural networks, to analyze various factors and optimize the lamp prediction process. Extensive testing and validation are conducted to assess the model's performance and efficiency. The findings demonstrate the superiority of the proposed model in terms of accuracy, efficiency, and cost-effectiveness compared to traditional methods. The study contributes to the field of lighting design by providing a reliable and automated solution that reduces human error and improves energy efficiency, occupant satisfaction, and safety. The findings of this research indicate that the 'trainlm' algorithm is the most effective for predicting the number of lamps needed. It achieved a regression value of 1.00 and a low error percentage of only 0.499%. These results demonstrate the algorithm's accuracy and suitability for the task at hand. The conclusion highlights the significance of the developed model in streamlining the design process and offers recommendations for future work, such as exploring additional factors and evaluating the model on diverse datasets. Overall, this research enhances the understanding and application of neural networks in lamp prediction for optimal lighting system designs. © 2023 IEEE.

关键词： Forecasting

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CNN Approach for Plant Disease Detection—Krishi Snehi 8th

CNN Approach for Plant Disease Detection—Krishi Snehi

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8th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2023

作者： Lavanya, A. Ganavi, N. Sowmya, M.R. Department of Computer Science and Engineering NMIT Yelahanka Bangalore560064 India

ISBN: (纸本)9789819976324

Agriculture is the main key component of the Indian economy. As a result, the role that food crops play crucial for both the environment and people. Many plants die as a result of poor disease diagnosis and lack of awareness of the disease’s symptoms, which directly affects the economy. India, being an agriculture-based country, farmers belong to the type of people, who are not much sound in both technical and English literacy, need scientific methods and proper knowledge on plant disease. This paper discusses the detection of plant disease from a plant leaf image using CNN algorithm and provides corresponding biological and chemical control measures in the user preferred language (English, Kannada, and Hindi) and those categories of farmers, who are unable to read/write even their own language, can gain access to detected disease and controls in speech format. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Agriculture

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Implementation of an automatic EEG Feature Extraction with Gated Recurrent Neural Network for Emotion Recognition 1

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4th international conference on Recent Trends in Advanced computing - Computer Vision and Machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

作者： Immanuel, Rajeswari Rajesh Sangeetha, S.K.B. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Chennai India

ISBN: (数字)9789811971693

ISBN: (纸本)9789811971686

Emotion is a complicated state that influences one's thoughts and behaviour. Recognizing the emotions of a human being is a major research interest in the affective computing after this pandemic situation and which can be applied in medical related fields to cure physical and mental illness. Early detection of stress helps humans to avoid or prevent many diseases related to it. The development of an emotion recognition system using machine learning algorithms has taken a lot of time and effort for researchers and is less focused with Electroencephalography (EEG) signals because EEG signals are noisy, non-linear, and non-stationary. Deep learning algorithms are the most popular solution due to its ability to see images as data. In this paper, we propose a deep learning framework, Gated Recurrent Unit Emotion Recognizer (GRUER) that can detect human emotion with help of EEG signals. This is achieved by implementing four feature extraction algorithms such as Short-Time Fourier Transform (STFT), Wavelet Entropy, Hjorth and Statistical features on dataset and the feature selection method Principal Component Analysis (PCA) is applied to the extracted features to select most significant features to obtain high-accuracy emotion recognizing model. Keras libraries are used to train the model in an appropriate way so that it is neither overfit nor underfit with the data using the Early-stopping function. The performance of the GRUER model is measured using performance metrics such as accuracy, precision, recall and F1-Score are illustrated in the results. The accuracy of the GRUER is 98% and it is a 3-dimensional model which has valence, arousal and dominance for emotion detection. The model loss obtained by GRUER is 1.12 which is low when compared to other models. Finally, the suggested method and its results show that this proposed method outperforms numerous existing emotion recognition systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte

关键词： Classification (of information)

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Miniature soft robots capable of multiform and multimodal Locomotion

Miniature soft robots capable of multiform and multimodal Lo...

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2024 IEEE international conference on Real-Time computing and Robotics, RCAR 2024

作者： Chen, Yinxi Yang, Zhan Lu, Bo Fan, Xinjian Soochow University School of Mechanical and Electrical Engineering Suzhou215131 China

ISBN: (纸本)9798350372601

Untethered microscale robots, capable of navigating through constrained spaces, offer immense potential for complex tasks like targeted drug delivery, micromanipulation, and environmental sensing, thanks to their diminutive size and wireless operation. However, traditional microscale robots, constructed from rigid materials, are limited by a scarcity of actuated degrees of freedom, making it challenging to support multiple modes of locomotion in complex environments. This paper introduces two types of microscale soft robots, controlled by magnetic fields, designed to circumvent the limitations of their rigid counterparts and enable a diverse array of movement patterns. We explore the shape transformations induced by magnetic fields and employ this mechanism to orchestrate the robots' movement, enabling them to walk, roll, and swim in a manner reminiscent of jellyfish. Furthermore, we optimized the shape of the small-scale swimming robot by intentionally increasing the width of its four legs compared to traditional cross-shaped swimming robots in previous studies. By widening the four legs, our jellyfish-like soft robot can generate greater thrust in water, optimizing its swimming performance and making its swimming behavior more controllable and efficient. Ultimately, we demonstrated the ability of these designed soft robots to navigate obstacles and autonomously traverse both two-dimensional (2D) and three-dimensional (3D) mazes, showcasing their adaptability and control in varied settings. © 2024 IEEE.

关键词： Targeted drug delivery

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Comparative Analysis of Rotor Position and Speed Extraction Schemes for Resolver Software Decoding in IPMSM Drive Applications 28

Comparative Analysis of Rotor Position and Speed Extraction ...

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28th international conference on Automation and computing, ICAC 2023

作者： Cheng, Linxuan Zhang, Tianru Chen, Yuqing Liu, Qinyao Wang, Xuchen Xu, Zhuang Sun, Jie School of Advanced Technology Xi'an Jiaotong-Liverpool University China University of Nottingham Department of Electrical and Electronics Engineering Ningbo China

ISBN: (纸本)9798350335859

In the automated manufacturing industry, resolvers are commonly employed in the control systems of interior permanent magnet synchronous motor (IPMSM) to derive rotor position and speed information, owing to their high precision and reliability. In resolver software decoding, the extraction method utilized for determining the rotor position and speed significantly affects the control performance of IPMSM system. In this paper, phase-locked loop (PLL), extended state observer (ESO) and third-order nonlinear extended state observer (TNESO) are applied respectively to construct the extraction scheme. This paper presents a comparative analysis of three extraction schemes, and the performance of them are compared through the simulation experiments. The results indicate that the ESO scheme achieves optimal performance in steady state, but experiences a delay during transient state. TNESO scheme, on the other hand, boasts the fastest transient response speed while also meeting steady state estimation accuracy criteria. In comparison to the aforementioned methods, the TNESO scheme exhibits more satisfactory overall performance. © 2023 IEEE.

关键词： Extraction

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Graphsage-Based Named Entity Recognition for Malayalam

Graphsage-Based Named Entity Recognition for Malayalam

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international conference on Robotics, control, Automation and Artificial Intelligence, RCAAI 2023

作者： Harikrishnan, M.K. Bindu, K.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819746491

The primary goal of a Named Entity Recognition (NER) system is to gather relevant information from the given text. NER in Malayalam text is challenging due to the agglutinative structure of the language. Due to the increasing presence of the Internet, more people use smartphones, leading to an increase in Malayalam search queries. A major portion of these searches contains Named Entities which makes it reasonable to study methods to identify them effectively. Words can have relations with other words in a sentence. If these relationships are considered as edges of a graph, the sentences could be processed as a graph instead of in a sequential manner. Graph Neural Networks (GNN) have been used for processing graphs in recent years. In this work, graphs for sentences in the Malayalam language are created using a simple nearest word method and processed in a GraphSAGE (Graph Sample and Aggregated)-based NER system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graph neural networks

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