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Maximum Application of Power Point Tracker on Microcontroller Based Dual Energy Solar Powered Dryer 3rd

Maximum Application of Power Point Tracker on Microcontrolle...

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3rd International Conference on Experimental and Computational Mechanics in engineering, ICECME 2021

作者： Jalil, Saifuddin Muhammad Husaini Munadi, Rizal Sara, Ira Devi Doctoral Program School of Engineering Post Graduate Program Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Darussalam Banda Aceh23111 Indonesia Department of Mechanical Engineering Universitas Syiah Kuala Darussalam Banda Aceh23111 Indonesia Department of Electrical Universitas Malikussaleh Lhokseumawe24351 Indonesia

ISBN: (纸本)9789811936289

Solar-powered dryers are still expensive and difficult to obtain for farmers with medium to low production capacity. For this reason, it is necessary to develop technology on a solar-powered dryer with a solar panel capacity of 200WP is equipped with a maximum power point tracker as an integrated solar collector with a photovoltaic system. The system in this solar-powered dryer consists of a solar panel tracker, a rotating aluminum cylinder net, a photovoltaic tracker, a battery bank, a microcontroller, and a DC/AC inverter. This system improves the performance of solar panels in the drying process of Moringa leaves. This design is expect to be the right system to ensure maximum results by providing solar radiation energy in the drying process of agricultural products. The aims goal to minimize problems in the drying of agricultural products experienced by farmers using an automatic dual-energy solar-powered dryer as a development innovation for the drying process. This study uses an experimental methodology and field data collection using a solar-powered dryer that has been analyzed and measured to obtain data comparisons. shows the maximum data of voltage and power response in positioning photovoltaic to produce the increased output power of photovoltaic array. Maximum application of powerpoint tracker on microcontroller based dual energy solar powered dryer on solar panels for drying moringa leaves produces the advantage of maximum energy absorption in solar panels and shortens the time in the drying process of moringa leaves. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Microcontrollers

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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Rahmania, Rissa Heryadi, Yaya Lukas, Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products. © 2023 IEEE.

关键词： Embeddings

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Hidayaturrahman Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusatara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies. © 2023 IEEE.

关键词： Adversarial Domain Adaptation Domain Shifting Problem MNIST Transfer Learning

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A Feasibility Study: Magnetic Field Monitoring in Cycled Lead Acid Cells for Stationary Applications

A Feasibility Study: Magnetic Field Monitoring in Cycled Lea...

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IEEE electrical Energy Storage Application and Technologies Conference (EESAT)

作者： Scarleth Vasconcelos Pritpal Singh Ross Lee Sustainable Engineering Program Villanova University Villanova USA Dept of Electrical and Computer Engineering Villanova University Villanova USA

ISBN: (数字)9798331524852

ISBN: (纸本)9798331536411

This research introduces a novel, real-time, non-invasive, non-destructive, and accurate method for battery mon-itoring using magnetic field mapping. This technique detects stratification in flooded Lead Acid Batteries at different states of charge, providing early notification by monitoring variations in hydrogen ion concentration during cycling. The study tracked pH and magnetic field variations across $H_{2}SO_{4}$ electrolyte specific gravities ranging from 1.07 to 1.33, showing a 17%-20% pH reduction over 12 hours, indicating acid stratification. A proof-of-concept using air-core solenoid coils demonstrated a direct correlation between secondary coil output voltage and $H_{2}SO_{4}$ concentrations, influenced by the induced magnetic field at the primary coil and electrolyte concentration changes. The optimal AC input frequency for maximizing the induced magnetic field mapping response was identified as 30–33 kHz, depending on the physical properties of the magnetic field inductors. An inverse correlation was also observed between the induced magnetic field and the distance between the coils. This approach offers a promising alternative for lead acid battery monitoring, potentially improving battery management and lifespan. The experimental tests also included monitored variations in secondary coil output voltage in a single flooded lead acid cell during cycling. The preliminary results demonstrated a decrease in the magnetic field during charge and an increase during discharge.

关键词： Coils Lead acid batteries Correlation Voltage measurement Electrolytes Discharges (electric) Lead compounds Magnetic fields Solenoids Monitoring

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RoboRover - A Jetson Nano Powered Robot Car for Object Following, Obstacle Avoidance, and Wall Following 14

RoboRover - A Jetson Nano Powered Robot Car for Object Follo...

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14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Huynh, Trinh Walter, James Aveta, Federica University of Michigan Robotics Department Ann ArborMI48109 United States Wentworth Insitute of Technology Electrical and Computer Engineering Program BostonMA02115 United States

ISBN: (纸本)9798350304138

An intelligent and self-sufficient robot is essential across a wide range of fields, including transportation, industry, space exploration, and defense. Mobile robots possess the capability to undertake diverse tasks such as handling materials, aiding in disaster scenarios, conducting patrols, and executing rescue operations. As a result, the development of an autonomous robot that can navigate through both unchanging and ever-changing surroundings has become important. The primary objective of mobile robot navigation revolves around ensuring the seamless and secure traversal of the robot through complex environments, starting from an initial position, and reaching a designated goal position. This paper presents the design and implementation of a Jetson Nano powered robot car which uses local sensors to interact with an unknown environment. Object following, obstacle avoidance, and wall following features are built for the car to navigate to reach its desired destinations. © 2023 IEEE.

关键词： Mobile robots

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Performance Evaluation of Deep Feature in Cross Spectral Face Recognition

Performance Evaluation of Deep Feature in Cross Spectral Fac...

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International Conference on Information Technology, computer and electrical engineering (ICITACEE)

作者： Ernita Dewi Meutia Fitri Arnia Nasaruddin Rusdha Muharar Maulisa Oktiana Khairun Saddami Department of Electrical and Computer Engineering Doctoral Program School of Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

Cross-spectral face recognition (CSFR), particularly in the Near Infra-Red (NIR) and visual (VIS) spectrum, presents challenges due to differences in illumination and sensor characteristics. NIR sensors are commonly used in practical applications, such as in nighttime outdoor surveillance systems, due to their ability to capture images in low light conditions and at various standoff distances. This study proposes a simple CSFR method that combines deep learning techniques with photometric normalization using Homomorphic Filtering (HF) to identify subjects in NIR images across different standoff distances. Both NIR and VIS images are filtered to mitigate illumination discrepancies. A deep convolutional neural network (CNN) model is employed to automatically extract discriminative deep features from the normalized images of both spectrums. Four deep CNN models of ResNet-50, DenseNet, EfficientNet, and MobileNetV2 are tested. The performance of the models in feature extraction for CSFR is evaluated on the LDHF (Long Distance Heterogeneous Face) database. The Hamming distance metric is employed to match NIR probe images with VIS gallery images. The experimental findings, expressed in recognition rate RR, demonstrate the effectiveness of the approach in enhancing CSFR accuracy, with MobileNetV2 achieving the highest accuracy of 90% for a short standoff. In addition, HF improves image quality, enhancing the deep learning model's ability to discern image characteristics.

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Residential Power Satisfaction as a Demand Side Management Approach in Puerto Rico

Residential Power Satisfaction as a Demand Side Management A...

引用

IEEE Central America and Panama Convention (CONCAPAN)

作者： Carlos A. Peña Becerra Willian Pacheco Cano Marcel Castro Sitiriche Samira Ortiz Department of Electrical and Computer Engineering University of Puerto Rico Mayaguez PR Electrical and Computer Engineering Program Universidad Ana G. Méndez – Recinto de Gurabo

ISBN: (数字)9798350366723

ISBN: (纸本)9798350366730

Puerto Rico's electrical infrastructure powers over three million residents, yet energy consumption overcomes generation and continues to rise. This imbalance results in an unreliable power supply and frequent blackouts across the island. One solution to this issue is to increase generation capacity by adding new generating units and enhancing transmission networks, though this is expensive. Another approach is to promote the adoption of rooftop solar generation with storage systems through microgrids. Alternatively, demand-side management (DSM) offers a more cost-effective solution by regulating demand and reducing energy use at the consumer level. This paper explores the unique integration of power satisfaction within DSM strategies to reduce energy consumption while preserving user satisfaction. Utilizing an agent-based model to simulate household appliance behavior, we demonstrate how power satisfaction can reduce energy demand. This innovative approach offers a promising pathway toward a more resilient and sustainable energy future for Puerto Rico.

关键词： Energy consumption Renewable energy sources Demand side management Power system stability Agent-based modeling Stability analysis Solar power generation Security Sustainable development Resilience

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Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting 38

Introducing Spectral Attention for Long-Range Dependency in ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kang, Bong Gyun Lee, Dongjun Kim, HyunGi Chung, DoHyun Yoon, Sungroh Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Department of Future Automotive Mobility Seoul National University Korea Republic of

Sequence modeling faces challenges in capturing long-range dependencies across diverse tasks. Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. However, they are constrained by their inherent inability to effectively address long-range dependencies in time series data, primarily due to using fixed-size inputs for prediction. Furthermore, they typically sacrifice essential temporal correlation among consecutive training samples by shuffling them into mini-batches. To overcome these limitations, we introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples and facilitates the handling of long-range information while maintaining the base model structure. Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture long-range dependencies over thousands of steps. Through extensive experiments on 11 real-world time series datasets using 7 recent forecasting models, we consistently demonstrate the efficacy of our Spectral Attention mechanism, achieving state-of-the-art results. © 2024 Neural information processing systems foundation. All rights reserved.

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Textual training for the hassle-free removal of unwanted visual data: case studies on OOD and hateful image detection 24

Textual training for the hassle-free removal of unwanted vis...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Saehyung Lee Jisoo Mok Sangha Park Yongho Shin Dahuin Jung Sungroh Yoon Department of Electrical and Computer Engineering Seoul National University Qualcomm Korea YH Seoul South Korea School of Computer Science and Engineering Soongsil University Department of Electrical and Computer Engineering and Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (纸本)9798331314385

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT

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Text2PointCloud: Text-Driven Stylization for Sparse PointCloud

Text2PointCloud: Text-Driven Stylization for Sparse PointClo...

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44th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2023

作者： Hwang, Inwoo Kim, Hyeonwoo Lim, Donggeun Park, Inbum Kim, Young Min Seoul National University Department of Electrical and Computer Engineering Korea Republic of Seoul National University Interdisciplinary Program in Artificial Intelligence and INMC Korea Republic of

ISBN: (纸本)9783038682097

We present Text2PointCloud, a method to process sparse, noisy point cloud input and generate high-quality stylized output. Given point cloud data, our iterative pipeline stylizes and deforms points guided by a text description and gradually densifies the point cloud. As our framework utilizes the existing resources of image and text embedding, it does not require dedicated 3D datasets with high-quality textures, which are produced by skillful artists or high-resolution colored 3D models. Also, since we represent 3D shapes as a point cloud, we can visualize fine-grained geometric variations with a complex topology such as flowers or fire. To the best of our knowledge, it is the first approach for directly stylizing the uncolored, sparse point cloud input without converting it into a mesh or implicit representation, which might fail to express the original information in the measurements, especially when the object exhibits complex topology. © 2023 Authors.

关键词： Textures

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