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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Sathiyaseelan J Deva Priya Isravel Julia Punitha Malar Dhas Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Renewable energy forecasting is crucial for maintaining grid stability, reducing curtailment, and optimizing energy utilization. However, forecasting wind and solar energy is challenging due to their intermittent and variable nature, influenced by complex weather patterns, non- linear dependencies, and seasonal variations. Additionally, issues like data quality, geographical differences, and computational constraints further complicate accurate predictions and integration with grid systems. This paper discusses the importance of renewable energy forecasting and evaluates the effectiveness of three predictive models: Long Short-Term Memory (LSTM), Facebook Prophet (F-Prophet), and AutoRegressive Integrated Moving Average (ARIMA) using real-world solar and wind energy data. The experimental results demonstrate that LSTM achieved a Mean Absolute Error (MAE) of 4.19, representing a 98.74% and 98.89% improvement over F-Prophet and ARIMA, respectively. For wind forecasting, LSTM recorded a remarkably low MAE of 0.056, outperforming F-Prophet and ARIMA by 99.99%. In terms of Root Mean Square Error (RMSE), LSTM also showed superior performance improving over F-Prophet and ARIMA by 71.05% and 76.88% in solar forecasting, and by 91.01% and 91.24% in wind forecasting. These results emphasize LSTM's effectiveness in capturing complex, non-linear relationships and temporal dependencies in energy generation data.

关键词： Renewable energy sources Accuracy Social networking (online) Wind energy Predictive models Power system stability Stability analysis Forecasting Wind forecasting Long short term memory

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User-Driven Customization in 3D Generation: Improving Stable Fast 3D with Inpainting Methods

User-Driven Customization in 3D Generation: Improving Stable...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Rahul A J. Anitha Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Stable Fast 3D is widely recognized for its remarkable capacity to generate 3D models from a single 2D image in as little as 0.5 seconds. This can be further improved upon by utilizing text-to-image latent diffusion especially using the inpainting technique in the stable diffusion. The purpose of this work is to improve the quality and fidelity of the generation of 3D models by allowing user-guided customizations during the reconstruction process. Inpainting confronts two significant challenges: incomplete or noisy input data, and visualization differences, by completing unobserved areas and improving input textures. Inpainting enables users to iteratively modify their inputs, and potentially provide more coherent and aesthetically pleasing final 3D models. Experimental results indicate that by utilizing inpainting incoporated with Stable Fast 3D, increases the model precision, while retaining the original speed of model generation. The method proposed in this paper expands the use of 3D reconstruction techniques to other domains including gaming, virtual reality, and product design by providing a solution that is both more interactive and easier to create high-quality 3D assets.

关键词： Solid modeling Visualization Three-dimensional displays Image resolution Computational modeling Lighting Virtual reality Hardware Usability Image reconstruction

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Advancing Hate Speech Detection: A Multilingual System Using LLAMA-2 for Real-Time Analysis of Audio, Video, and Text Content

Advancing Hate Speech Detection: A Multilingual System Using...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： MD Talha J A M Rexie Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This paper introduces an advanced, multilingual hate speech detection system designed to process various media formats, including audio, video, and text. Unlike traditional detection models that focus solely on text analysis, our system leverages OpenAI's Whisper model to convert audio and video content into text, enabling a broader scope of analysis. At its core, the system utilizes a fine-tuned Llama-2 model specifically trained for hate speech classification. The detection system is accessible through a user-friendly Streamlit web application, providing real-time processing and feedback. Results demonstrate that this comprehensive, multimedia approach significantly enhances detection accuracy and usability, marking a notable advancement in the field.

关键词： Analytical models Text analysis Visual analytics Multimedia systems Hate speech Streaming media Real-time systems Multilingual Usability Audio-visual systems

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Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

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computers, Materials & Continua 2023年第3期74卷 6263-6287页

作者： Arjmand Khaliq Syed Abdul Rahman Kashif Fahad Ahmad Muhammad Anwar Qaisar Shaheen Rizwan Akhtar Muhammad Arif Shah Abdelzahir Abdelmaboud Electrical Engineering Department University of Engineering and TechnologyLahore54700Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia Department of Information Science Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan Sub CampusPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia

Vector control schemes have recently been used to drive linear induction motors(LIM)in high-performance *** trend promotes the development of precise and efficient control schemes for individual *** research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation(SVPWM)*** framework under consideration is developed in four *** begin,MATLAB Simulink was used to develop a detailed mathematical and electromechanical *** research presents a modified SVPWM inverter control *** tuning the proportional-integral(PI)controller with a transfer function,optimized values for the PI controller are *** the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control *** reference speed values were chosen to evaluate the performance of the respective system,and the developed system’s response was verified using various data *** the low-speed range,a reference value of 10m/s is used,while a reference value of 100 m/s is used for the high-speed *** speed output response indicates that themotor reached reference speed in amatter of seconds,as the delay time is between 8 and 10 *** maximum amplitude of thrust achieved is less than 400N,demonstrating the controller’s capability to control a high-speed LIM with minimal thrust *** to the controlled speed range,the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.

关键词： Space vector pulse width modulation linear induction motor proportional-integral controller indirect vector control electromechanical dynamic modeling

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Smart Farming with Improved Security using Ascon Encryption and Authentication

Smart Farming with Improved Security using Ascon Encryption ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Rahul R R. Venkatesan T. Jemima Jebaseeli Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore

The evolution of smart farming through the integration of Internet of Things (IoT) technology has ushered in a new era of precision agriculture, offering increased efficiency, sustainability, and productivity. However, this technological advancement also brings forth a critical concern: the security of the data collected and transmitted by IoT devices in agricultural settings. In response to this concern, this research presents a comprehensive security implementation tailored for IoT-based smart farming systems. At its core, the system focuses on two key aspects: data authentication and secure transmission. To achieve these objectives, the Ascon encryption algorithm, known for its lightweight design and robust security features has been proposed. The implementation utilizes Raspberry Pi devices, powered by the Adafruit CircuitPython library, to collect real-time sensor data from various agricultural sources. This data encompasses a wide range of vital parameters, including temperature, humidity, soil moisture, and livestock health. The Ascon algorithm is employed for device authentication, ensuring that only authorized devices gain access to the IoT network. The crux of the research lies in securing the data from its point of origin to its final destinations. The collected sensor data undergoes encryption using the ASCON algorithm before transmission. This encryption guarantees the confidentiality and integrity of the data, making it immune to interception or tampering during transit. AskSensors cloud platform acts as the secure repository for this encrypted data, while mobile integration provides users with real-time access to critical agricultural insights. This research represents a vital stride in addressing the pressing security challenges that accompany 10T-based smart farming. By combining the robust authentication capabilities of the Ascon algorithm with the secure data transmission to AskSensors, establish a trust framework essential for the widespread adop

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Near-Perfect Coverage Manifold Estimation in Cellular Networks via Conditional GAN

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 97-100页

作者： Mondal, Washim Uddin Goyal, Veni Ukkusuri, Satish V. Das, Goutam Wang, Di Alouini, Mohamed-Slim Aggarwal, Vaneet Purdue University School of Industrial Engineering The School of Civil Engineering West LafayetteIN47907 United States Purdue University School of Civil Engineering West LafayetteIN47907 United States Indian Institute of Technology Kharagpur G. S. Sanyal School of Telecommunications Kharagpur721302 India Purdue University School of Industrial Engineering West LafayetteIN47907 United States King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences and Engineering Division Thuwal23955-6900 Saudi Arabia

This letter presents a conditional generative adversarial network (cGAN) that translates base station location (BSL) information of any Region-of-Interest (RoI) to location-dependent coverage probability values within a subset of that region, called the region-of-evaluation (RoE). We train our network utilizing the BSL data of India, the USA, Germany, and Brazil. In comparison to the state-of-the-art convolutional neural networks (CNNs), our model improves the prediction error (L1 difference between the coverage manifold generated by the network under consideration and that generated via simulation) by two orders of magnitude. Moreover, the cGAN-generated coverage manifolds appear to be almost visually indistinguishable from the ground truth. © 2019 IEEE.

关键词： Stochastic systems

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980 nm micro-cavity lasers with sub-milliampere threshold

980 nm micro-cavity lasers with sub-milliampere threshold

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Lin, Qi Xue, Ying Huang, Jie Gu, Wen Lau, Kei May Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong Division of Emerging Interdisciplinary Area Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We report electrically-pumped InGaAs/GaAs/GaAsP quantum well micro-cavity lasers on GaAs with low thresholds down to the sub-milliamp level and a maximum operating temperature of 95°C. Micro-lasers with various c... 详细信息

关键词： III-V semiconductors

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Lithology Identification of Improved Model based on DeepLabv3 + Deep Convolutional Neural Network

Lithology Identification of Improved Model based on DeepLabv...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Wang, Ju Yin, Senlin Hu, Zhangming Bai, Kai College of Computer Science and Technology Yangtze University Hubei China Research Institute of Mud Logging Technology and Engineering Yangtze University Hubei China Hubei Provincial Key Laboratory of Southern Complex Shale Oil and Gas Geology and Development Hubei Province Wuhan China Xibu Drilling Engineering Company Limited Engineering and Technology Division of CNPC China

ISBN: (纸本)9798331517021

Aiming at the complexity of outcrop lithology identification, an improved outcrop lithology identification algorithm based on DeepLabv3 + is proposed. We use the Xception network replaced by the MobileNetV2 module to enhance the ability to extract lithologic features and make the model more lightweight. In order to segment pixels more accurately, the CA (Coordinate attention) attention mechanism is introduced, so that the improved backbone network can extract lithologic features more accurately in complex environments. The experimental results show that compared with the traditional DeepLabv3 +, the accuracy of the improved algorithm is increased by 1.08 percentage points, the MP A is increased by 2.4 percentage points, and the average classification accuracy is 94.86 %. Compared with other mainstream semantic segmentation models, all aspects of performance are more prominent. © 2024 IEEE.

关键词： Semantic Segmentation

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980 nm micro-cavity lasers with sub-milliampere threshold

980 nm micro-cavity lasers with sub-milliampere threshold

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Semiconductor quantum wells

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A Translation-Tolerant Place Recognition Method by Viewpoint Unification 27

A Translation-Tolerant Place Recognition Method by Viewpoint...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zheng, Linwei Hu, Xiangcheng Ma, Fulong Zhao, Guoyang Qi, Weiqing Ma, Jun Liu, Ming The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong

ISBN: (纸本)9798331505929

Place recognition serves as a fundamental component in tasks like loop closure detection and relocalization for mobile robots. Polar coordinate representations, such as Scan Context, which align with the data structure of range sensors, have become the most common data structure for point cloud descriptors in place recognition. While polar representations demonstrate the rotation invariance, they remain susceptible to translation variations. In this study, we introduce a novel approach: shifting the viewpoint of the original point cloud to construct the unified Scan Context, thereby mitigating translation variance. Our key concept focuses on identifying a stable, unified viewpoint for a given place and then pre-translating the point cloud accordingly. This naturally results in a descriptor devoid of translation variance. Importantly, within a given place, the viewpoint unification process tends to relocate the viewpoint to a similar position, irrespective of the original sensor perspective. In other words, the unified Scan Context becomes more closely associated with the place's structural characteristics than the physical location of the sensor. We validate our method through a series of comprehensive experiments encompassing synthetic scenarios and real-world datasets, showcasing its robustness in effectively handling translation variations. © 2024 IEEE.

关键词： Data structures

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