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To Predict the Fire Outbreak in Australia using Historical Database 10

To Predict the Fire Outbreak in Australia using Historical D...

10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Tayal, Devendra Kumar Agarwal, Nidhi Jha, Anjali Deepakshi Abrol, Vrinda Indira Gandhi Delhi Technical University for Women Dept. of Computer Science & Engineering New Delhi India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

Australian bush fires have caused huge damage, not only to the environment but also to the community. In southeastern Australia, 11.5 million hectares (28.4 million acres) of bushland and forest was affected during the 'Black Summer' Bushfire of 2019-2020. The frequency of bushfires gives context for modelling different climate data to accurately anticipate future hot regions for bushfires. In this study, we have implemented a Machine Learning based Decision Tree Model to construct a Forest Fire Prediction Model using data from the last 20 years. This algorithm is derived from a collection of unrelated decision trees. Additionally, we converted observed fire spots into a continuous density of fire spots and made a choropleth map. The prediction model's structure makes it possible to produce predictions with a higher degree of accuracy. Additionally, it helps to increase assistance for fire crews at the front-line. © 2022 IEEE.

关键词： Fires

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Embodied AI in Mobile Robots: Coverage Path Planning with Large Language Models

arXiv

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arXiv 2024年

作者： Kong, Xiangrui Zhang, Wenxiao Hong, Jin Braunl, Thomas Dept. of Electrical Electronic and Computer Engineering The University of Western Australia Perth Australia Dept. of Computer Science and Software Engineering The University of Western Australia Perth Australia

In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and solving mathematical problems, leading to advancements in various fields. We propose an LLM-embodied path planning framework for mobile agents, focusing on solving high-level coverage path planning issues and low-level control. Our proposed multilayer architecture uses prompted LLMs in the path planning phase and integrates them with the mobile agents' low-level actuators. To evaluate the performance of various LLMs, we propose a coverage-weighted path planning metric to assess the performance of the embodied models. Our experiments show that the proposed framework improves LLMs' spatial inference abilities. We demonstrate that the proposed multi-layer framework significantly enhances the efficiency and accuracy of these tasks by leveraging the natural language understanding and generative capabilities of LLMs. Our experiments show that this framework can improve LLMs' 2D plane reasoning abilities and complete coverage path planning tasks. We also tested three LLM kernels: gpt-4o, gemini-1.5-flash, and claude- 3.5-sonnet. The experimental results show that claude-3.5 can complete the coverage planning task in different scenarios, and its indicators are better than those of the other models. © 2024, CC BY-NC-SA.

关键词： Motion planning

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Performance Analysis of Single Ended Primary Inductor Converter with Adaptive and Static Proportional Integral Control Variants

Performance Analysis of Single Ended Primary Inductor Conver...

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International Conference on Electrical engineering, computer science and Informatics (EECSI)

作者： A. Nur Annisa Alif Panawan Faizal Arya Samman Ida Rachmaniar Sahali Kayue M. Smedley Dept. of Electrical Engineering Hasanuddin University Gowa Indonesia Dept. of Electrical Engineering and Computer Science University of California Irvine California

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

The Single-Ended Primary-Inductor Converter (SEPIC) is one type of DC-DC converter that has been widely utilized. In certain conditions, this DC-DC converter may exhibit inappropriate behavior regarding stability and reliability, thus requiring a controller to ensure that the voltage, current, or power values they produce align with the desired one. In this research, we examine and simulate the performance of the SEPIC converter using a Proportional Integral (PI) controller and compare the use of tuning methods: Ziegler-Nichols (ZN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Fuzzy Logic in regulating the PI control parameters. Simulations will be performed through MATLAB. From the simulation results, it is observed that the PI tuning methods using GA, PSO, and Fuzzy Logic are superior to the ZN method, and with the advantage of adaptive process for PI tuning, the fuzzy logic method outperforms in terms of convenience to the other three methods.

关键词： Fuzzy logic Simulation DC-DC power converters Stability analysis Steady-state Reliability Particle swarm optimization Voltage control Tuning Genetic algorithms

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Adaptive Traffic Signal Timing: Leveraging YOLOv10 and computer Vision for Real-Time Optimization

Adaptive Traffic Signal Timing: Leveraging YOLOv10 and Compu...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Suhas Papanashi Manya Chadaga Kshithi R Santosh S Huddar K Sreelakshmi Ramakanth Kumar P Dept. of Electronics and Telecomm RV College of Engineering Bengaluru Dept. of Computer Science & Eng. RV College of Engineering Bengaluru

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Traffic congestion poses a significant challenge in modern urban environments, particularly in densely populated cities. Traditional traffic signals, which operate on fixed timings, often require manual adjustments to respond to varying traffic densities, leading to inefficient traffic flow. This issue arises because fixed intervals do not account for the dynamic nature of traffic patterns throughout the day. While developing intelligent systems that adjust signal durations dynamically offers a potential solution, such systems involve substantial financial investment and extended implementation timelines. This paper proposes a method to optimize green light durations by analyzing footage collected from a traffic junction in Bengaluru. This analysis can be performed using existing CCTV footage or manually gathered data. Even limited sample sizes can provide valuable insights for preliminary adjustments. The approach utilizes YOLOv10, a state-of-the-art computer vision algorithm, to extract relevant traffic data. The data is processed by an algorithm designed to reallocate green light durations, which are then compared with the original timings. An experimental setup involved recording one-minute videos over three days at the selected junction and applying the proposed algorithm to assess and refine signal durations. The results demonstrate the feasibility of using such algorithms to improve traffic signal management effectively.

关键词： computer vision Urban areas Green products Traffic control Real-time systems Timing Data mining Junctions Vehicle dynamics Videos

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Data-Enhanced Prediction with Decomposition and Amplitude-Aware Permutation Entropy in Distributed Computing Systems

Data-Enhanced Prediction with Decomposition and Amplitude-Aw...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haitao Yuan Qinglong Hu Jing Bi Wei Zhang Jia Zhang MengChu Zhou School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science City University of Hong Kong Hong Kong China College of Computer Beijing University of Technology Beijing China Dept. of Computer Science and Engineering University of Connecticut Storrs CT USA Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In recent years, distributed computing has wit-nessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and inter-dependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVI for achieving high-accuracy workload prediction in distributed computing systems. SVI employs the Savitzky-Golay filter and variational mode decomposition for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVI improves the prediction accuracy by 35.4% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVI enhances the prediction accuracy of workload, CPU, and memory by 62.5%, 65.6%, and 69.1 %, respectively.

关键词： Training Accuracy Costs Computational modeling Memory management Organizations Quality of service Predictive models Market research Internet

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Secure and Traceable QR Code Using Blockchain-enabled Certificates 11

Secure and Traceable QR Code Using Blockchain-enabled Certif...

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11th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2022

作者： Bhadoria, Robin Singh Goyal, Akshat Das, Arka Prabha Bashar, Abul Zikria, Mohammed Gla University Dept. of Computer Engineering and Applications Mathura India Dept. of Cse Mathura India Bhopal India Prince Mohammad Bin Fahd University College of Computer Engineering and Science Khobar Saudi Arabia

ISBN: (纸本)9781665480383

The blockchain has been considered to be a breakthrough technology solution for problems based on security aspects. It is a Distributed Ledger Technology (DLT) which can be used to established trust among different trustless entities or nodes present in the network. Furthermore, Quick Response (QR) Codes are likewise one of the most promising innovations going on in the markets. In QR code, small dots are present which get converted to binary numbers and get validated with error correcting algorithms. Hence, QR codes are beneficial for both storage and authentication. In this article, we discuss the prospects of combination of these two technologies for better results. In this paper, we aim to develop permission-less Blockchain, which is relatively easier to compute and have shown to increase its data capacity. It also endeavored to add the QR Code incorporation to a block and make it more scalable with considering the limited space in a such block. © 2022 IEEE.

关键词： Blockchain

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High-Performance Face Identification Using Randomly Selected Channels from Hyperspectral Images

High-Performance Face Identification Using Randomly Selected...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Sota Furusawa Chinthaka Premachandra Dept. of Electrical Engineering and Computer Science Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into “learned subject (class)” and “unlearned subjects (classes)”. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments.

关键词： Accuracy Passwords Biometric authentication Generative adversarial networks Search problems Security Protection Information technology Faces Hyperspectral imaging

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Human Activity Recognition using Smartphone Dataset: A Review

Human Activity Recognition using Smartphone Dataset: A Revie...

引用

2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Verma, Apurv Verma, Vijayant Patel, Brijesh Dept. of Computer Science and Engineering School of Engineering and I.T. MATS University Raipur India Dept. of Mechanical Engineering School of Engineering and I.T. MATS University Raipur India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Cell Phones are becoming more refined with each new model release. These days phones ordinarily consolidate numerous sensors for various functions such as GPS tracking, temperature sensors, accelerometer, gyroscope, high-determination cameras. In recent years, Human Activity Recognition (HAR) has enabled various applications. The information gathered by HAR may be utilized for a number of reasons, including fitness tracking, health and safety, and entertainment, among others. An abundance of research has been conducted on real-time processing, which has resulted in increased power consumption of mobile devices. It is preferable that the activity identification process be as precise as possible, because of the resource limited feature of a mobile device. So, the research is to use the dataset from the smartphone and use various classification models to find out which classifieris more accurate and provides fasterresults. © 2022 IEEE.

关键词： Smartphones

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Analysis of Common Mode Currents and Harmonic Pollution at Supplying Induction Motors from Static Converters with Variable Modulation Frequency

Analysis of Common Mode Currents and Harmonic Pollution at S...

引用

2023 Power Quality and Electromagnetic Compatibility at Low-Frequency, PQEMC-LF 2023

作者： Nuca, Iurie Wan, Lu Nicolae, Petre - Marian Nicolae, Ileana - Diana Grassi, Flavia Wu, Xinglong University of Craiova Electrical Engineering Energetic and Aeronautics Dept. Craiova Romania Politecnico di Milano Department of Electronics Information and Bioengineering Milan Italy University of Craiova Computer Science and Information Technology Dept. Craiova Romania

ISBN: (纸本)9798350341331

The problems of Electromagnetic Compatibility (EMC) in three-phase systems are less studied than single-phase or direct current, due to the smaller ratio of emissions relative to the rated power of such systems. This paper is focused on the problem of emissions in three-phase variable frequency drive systems in the low-frequency EMC range, specifically on the common mode current through the connected ground wire between the static converter and power grid. The common mode current is determined to be variable depending on switching frequency. For a deeper understanding Fast Fourier Transform was used to decompose the signal. Main parameter to understand the magnitude of common mode current was the RMS of the current. The decomposition showed a significant DC component which cannot be picked up by the used RF probe, thus presenting a noise offset necessary to be considered when calculating the total effective value of common mode current. The analysis was made on the time-domain representation and the frequency behavior of the converter. The paper suggests that the standard measurement in the low-frequency range and the picked-up signal are investigated enough, as there is a disparity in the impact of emissions in the low-frequency range compared to what off-the-shelf devices have to offer, which are standard compliant, but still present considerable emissions. © 2023 IEEE.

关键词： Static converters

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An Efficient Salt and Pepper Noise Reduction Approach for Video(s) Using Optimized Filter Approach 2

An Efficient Salt and Pepper Noise Reduction Approach for Vi...

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2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2022

作者： Kumain, Sandeep Chand Singh, Maheep Govil, Mahesh Chandra Pilli, Emmanuel Shubhakar Srinagar India Malaviya National Institute of Technology Jaipur Dept. of Computer Science and Engineering Jaipur India

ISBN: (数字)9781665488754

ISBN: (纸本)9781665488754

In today's digital world, most information is shared through images or videos. Images and videos are more infor-mative than textual data. During the image or video capturing process, addition of noise is one of the major problems. The presence of noise in captured data causes results to be misleading, particularly in computer vision and image processing tasks. As a result, preprocessing is required before tackling tasks like edge detection, object detection, object recognition, salient object detection, video summarization, and so on. Gaussian, Salt & Pepper, Poison, and Speckle noise are the most common types of noise which can affect the video. In this article, the author(s) presented an efficient noise reduction approach for reducing the salt and pepper noise present in the image or video. The performance of the proposed approach is compared with traditional and widely used filters for noise reduction, such as mean, mode, and median. The experimental analysis of the proposed approach is done on the dataset where the videos are captured using the camera as well as from the other resources such as Internet. Noisy videos are prepared by mixing the Salt & Pepper noise with different noise densities (levels) in the video dataset. The experimental results show that the proposed approach outperforms the traditional approaches not only in terms of noise reduction but also in preserving the details in the video. © 2022 IEEE.

关键词： Salt and pepper noise

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