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Estimation of Power system Harmonics Using Least Square Based Arithmetic optimization Algorithm

Estimation of Power System Harmonics Using Least Square Base...

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International Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Aftab Asghar Muhammad Mansoor Ashraf Electrical Engineering Department University of Engineering & Technology Taxila Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

Harmonic distortion stands out as a significant power quality issue in contemporary power systems that experience extensive utilization of renewable energy sources. this concern has garnered increased focus in recent years due to the growing integration of power electronic devices and nonlinear loads within the power systems. For electric utilities and users, accurate estimation of harmonic-parameters is an important reference to evaluate power quality. the detrimental impacts of harmonics components on power systems can be effectively mitigated by Active Filters (AFs). the performance of AFs heavily relies on the development of robust and accurate estimator, which plays a vital role in supplying the necessary reference harmonic values. this paper presents a Least Square based Arithmetic optimization Algorithm (LS-AOA) to solve the harmonics estimation problem. the AOA leverages the distribution characteristic of main arithmetic operators, such as division, subtraction, multiplication and addition in mathematics. AOA is mathematically formulated and implemented to efficiently optimize search processes across diverse ranges. the proposed LS-AOA framework is tested and validated on benchmark test signals with different noise levels and on practically measured voltage waveform of AFPMG. the experimental results demonstrated that the proposed harmonic estimator provides more accurate estimations with significantly lower computational time compared to existing techniques proposed in literature.

关键词： Renewable energy sources Accuracy Voltage measurement Power quality Estimation Harmonic analysis Real-time systems Power harmonic filters optimization Arithmetic

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Development of low-cost unmanned surface vehicle system for bathymetric measurements 20

Development of low-cost unmanned surface vehicle system for ...

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20th conference of PhD Students and Young Scientists

作者： Wajs, J. Kasza, D. Wroclaw University of Science and Technology 27 Wyb. Wyspiańskiego St. Wroclaw50-370 Poland

Bathymetric measurements are a significant branch of geodesy. they provide information required for the modeling of terrain topography below the water level for both running (lotic) and standing (lentic) waters. the existing technology allows the development of autonomous bathymetric systems which integrate Global Navigation Satellite systems (GNSS) sensors, an echosounder/sonar and a floating platform. Such a solution has been designed at Wroclaw University of Science and Technology. BATDRON – an integrated bathymetric system – is a low-cost solution. the hull is in the form of a catamaran, which ensures high stability during measurements. the navigation module was based on the Ardupilot APM platform and was integrated with a propulsion system consisting of two T200 thrusters manufactured by Bluerobotics. the main sensor set uses a Satlab SLD-200 singe beam echosounder synchronized with a Trimble R6 GNSS antenna for both navigation and recording the position of the echosounder pole. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Navigation

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Educational Group Recommendations with Virtual Leaders 20

Educational Group Recommendations with Virtual Leaders

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20th IEEE International conference on Advanced Learning Technologies (ICALT)

作者： Zheng, Yong IIT Dept Informat Technol & Management Chicago IL 60616 USA

ISBN: (纸本)9781728160900

Recommender systems have been applied in the area of education to suggest learning materials, assist informal learning, recommend projects or advisors, and so forth. In the process of teaching or learning, students may work together in a group. In this case, the group recommendations may come into play. In this paper, we propose to build a novel group recommendation model for education by introducing a virtual leader to each group. More specifically, we represent these virtual leaders by the personality traits, and learn these representations towards the optimization of the recommendations. Our experimental results demonstrate the effectiveness of the proposed models in comparison with other group recommendation models.

关键词： recommender system group recommendation personality trait virtual leader

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Managing Just-In-Sequence micro-logistics with Multi-Agent-Path-Finding

Managing Just-In-Sequence micro-logistics with Multi-Agent-P...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Matevž Bošnak Andrej Zdešar Rok Vrabič Gregor Klančar Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia Faculty of Mechanical Engineering University of Ljubljana Ljubljana Slovenia

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In the world of modern manufacturing and logistics, efficient and synchronized processes are crucial for optimized operations. Just-In-Sequence (JIS) deliveries to assembly lines and/or packaging stations demand precise sequencing of components and items, highlighting the significance of Multi-Agent Pathfinding (MAPF). Traditional MAPF approaches often overlook the order of arrival, rendering them mostly inappropriate for target applications, where such ordering is required. To address this, our research explores a nuanced MAPF approach that incorporates deadlines and pick-deliver-dock sequences, leveraging graph-based maps for continuous, real-time planning. Lifelong MAPF systems, distinguished mostly by their online task planning and continuous task list execution, excel in dynamic environments with constant arrival of new orders and tasks. Our contributions extend to refining Safe Interval Path Planning (SIPP) with priorities, suboptimal composited tasks, and recursive SIPP, ensuring reliable task planning and execution over extended periods. We delve into task decomposition techniques for efficient pick-up and drop-off sequencing, crucial for real-world MAPF optimization. By bridging theory with practical applications, our methodologies propel the efficiency and applicability of MAPF in realistic environments, offering insights for modern logistics and manufacturing challenges.

关键词： Sequential analysis Reviews Refining system recovery Reliability theory Rendering (computer graphics) Real-time systems Planning Synchronization Logistics

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A Machine Learning-driven IoT Architecture for Predicting the Growth and Trend of Covid-19 Epidemic Outbreaks to Identify High-risk Locations

A Machine Learning-driven IoT Architecture for Predicting th...

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Learning and Technology conference (L&T)

作者： Charles Rajesh Kumar. J Mary Arunsi. B M. A. Majid Department of Electrical and Computer Engineering College of Engineering Effat University Jeddah Saudi Arabia Chinmaya Mission Hospital Bengaluru Karnataka India

Covid-19 has had a destructive influence on global economics, social life, education, and technologies. the rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. this research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. the proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. the gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. the regression technique is utilized for prediction. the outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. the NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. these algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. this approach generates automatic notifications to concerned government authorities in any aberrant de

关键词： COVID-19 Machine learning algorithms Pandemics Biological system modeling Clustering algorithms Artificial neural networks Prediction algorithms

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Information value measurement distribution model based on improved information entropy 4

Information value measurement distribution model based on im...

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4th IEEE International conference on Electronic Information and Communication Technology, ICEICT 2021

作者： Chen, Liang Chen, Chilian Zhao, Lulu Key Laboratory of Technology on Data link China Electronics Technology Group Corporation 20th Institute Xi’an China

ISBN: (纸本)9781665432030

in order to meet the needs of modern war for high-quality situation information, this paper first proposes a battlefield situation information value measurement model based on improved information entropy from the perspective of battlefield situation information. According to the situation information index system, the degree of information demand satisfaction, and the principle of minimum error, the information entropy is improved to realize the battlefield situation information value measurement. then, referring to Link16’s distributed route selection algorithm based on R2 reporting responsibility, a message distribution algorithm based on information value is designed to give priority to the transmission of high-value information flow in the network. Finally, the effectiveness of the algorithm is verified by the simulation of typical combat scenarios of close air support, especially in the case of strong confrontation environment. © 2021 IEEE

关键词： Weight measurement Atmospheric modeling conferences Measurement uncertainty Interference Time measurement Information and communication technology

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Optimal Parameter Estimation of Solar Cell using Simulated Annealing Inertia Weight Particle Swarm optimization (SAIW-PSO)

Optimal Parameter Estimation of Solar Cell using Simulated A...

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20th IEEE International conference on Environment and Electrical Engineering (EEEIC) / 4th IEEE Industrial and Commercial Power systems Europe conference (I and CPS Europe)

作者： Kiani, Arooj Tariq Nadeem, Muhammad Faisal Ahmed, Ali Sajjad, Intisar Ali Hans, Muhammad Sohaib Martirano, Luigi Univ Engn & Technol Taxila Dept Elect Engn Taxila Pakistan Bahria Univ Dept Elect Engn Islamabad Pakistan Sapienza Univ Roma Dept Astronaut Elect & Energy Engn DIAEE Rome Italy

ISBN: (纸本)9781728174556

the operation of Photovoltaic (PV) system mainly rely on appropriate modeling of solar cells and optimum approximation of parameters associated with them. Recently, various hybrid, numerical and analytical techniques were proposed to extract optimal parameters of PV cell. this paper presents an efficient approach, A Simulated Annealing Inertia Weight Particle Swarm optimization (SAIW-PSO) for optimal estimation of PV parameters for double and single diode models. In addition, fitness indicator is guided using the Newton Raphson Method (NRM) that supports SAIW-PSO to explore the optimal solution. the premature convergence problem of typical PSO is resolved by the proposed framework. the strength of proposed approach is validated under standard test conditions (STC) on RTC France Silicon Solar cell. the SAIW-PSO is capable to explore optimum solution in smaller number of iterations and less computation time. the obtained results clearly depict that the proposed framework is fast, efficient and much accurate for PV cells parameters approximation.

关键词： parameter estimation double and single diode models photovoltaic Simulated Annealing inertia weight particle swarm optimization Root mean square error

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FELSDiff: Image Super-Resolution Based on Diffusion Model Fusing Feature Extraction with Latent Space

FELSDiff: Image Super-Resolution Based on Diffusion Model Fu...

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International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Cancan Wu Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In addressing issues like an excessive number of network parameters and the loss of fine texture details in image super-resolution reconstruction approaches based on diffusion models, this passage introduces a novel image super-resolution model (FELSDiff). this model relies on the fusion of diffusion models, feature extraction, and latent space. FELSDiff utilizes a Pre-trained CNN for initial super-resolution to extract the primary low-band features and part high-band features of the image. the preliminary super-resolved image is then concatenated as a condition to this model. We introduce a cross-attention mechanism guided by the high-band features of the preliminary super-resolved image to enhance the network's feature extraction capability and obtain more effective features. In the U-Net structure, we incorporate a latent space strategy, enabling the model's training and inference processes to be conducted in the latent space, thereby reducing the number of network parameters. Experimental results on multiple standard datasets indicate that this method outperforms other advanced methods overall.

关键词： Training Runtime Computational modeling Superresolution Memory management Feature extraction Diffusion models Convolutional neural networks Image reconstruction Standards

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Research on Enhanced Perception and Attention-Driven Person Re-Identification

Research on Enhanced Perception and Attention-Driven Person ...

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International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Cancan Wu Dong Li Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Hubei China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

When matching similarity among pedestrians in images, pedestrian re-identification algorithms are often disturbed by occlusions. A typical tactic is to improve the robustness of occlusion features in the model. However, this approach neglects the noise introduced by other pedestrians in the image, leading to limited learning of relevant information between images. To address these issues, this paper introduces a pedestrian re-identification model that leverages perception-enhanced and attention mechanisms. By enhancing the perception of non-pedestrian noise and erasing non-pedestrian features, we then employ a cross-attention mechanism to subtly embed global and local features for finer feature recognition. Finally, through context attention, we learn the interaction between pedestrian images to improve re-identification accuracy. Results from studies performed on the Market-1501, CUHK03-D, and Duke-ReID datasets indicate that the suggested model surpasses leading contemporary models regarding Rank-1 scores and mean Average Precision (mAP), while maintaining good robustness.

关键词： Pedestrians Accuracy Attention mechanisms Computational modeling Noise Knowledge discovery Robustness Noise measurement Identification of persons Fuzzy systems

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Deposition Width Prediction in Extrusion-based Bioprinting using Deep Learning with thermal Imager Temperature Series

Deposition Width Prediction in Extrusion-based Bioprinting u...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Chao-Yaug Liao Ting-Wei Hsueh Department of Mechanical Engineering National Central University Taoyuan Taiwan

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Many scaffolds in tissue engineering applications are fabricated using extrusion-based bioprinters with temperature-sensitive material. this method requires precise control of the deposition environment's temperature to allow a stable material formation, as these novel materials are temperature-sensitive. However, challenges arise in measuring the material deposition temperature during the entire printing process. therefore, this study focuses on solving this limitation using a thermal imager. First, before printing the next layer, a thermal image is captured above the platform to rapidly obtain the temperature distribution of the two-dimensional material deposition surface. Subsequently, through coordinate conversion, the temperature values passed by the moving path of the nozzle are sequentially obtained to establish an orderly deposition temperature series. After that, a deep learning model is developed to find the relationship between printing parameters and the deposition linewidth of the filament. this study collected 348 data, of which 298 were used for the training set and 50 for the validation set. the R-square was 98.5%, and RMSE is 0.01 mm, validating the model learning results. Findings provide insights that can be used to fine-tune the material extrusion pressure and nozzle moving speed in each line segment during bioprinting to obtain a suitable morphology scaffold with consistent porosity.

关键词： Temperature measurement Printing Deep learning Training Bioprinting Temperature distribution Rapid thermal processing Biological system modeling Tissue engineering Morphology

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