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Performance of Building-Integrated Photovoltaic and Addressing the Challenges of Color, Safety, and Light-Emitting Diode Integration

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Physica Status Solidi (A) Applications and Materials Science 2025年第10期222卷

作者： Mohammed, Shurouq Abdulqadir Yousuf, Hasnain Jony, Jaljalalul Abedin Khokhar, Muhammad Quddamah Rahman, Rafi Ur Aida, Maha Nur Alamgeer Park, Sangheon Yi, Junsin Department of Electrical and Computer Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of Department of Future Energy Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of

Building-integrated photovoltaics (BIPVs) provide a dual-purpose solution, functioning as both energy-generating systems and integral building materials. BIPV adoption has been limited due to a lack of color integration, preventing the panels from being used in building designs. However, efforts to incorporate color into the BIPV layers have been made, but they often reduce panel efficiency. BIPV implementation faces several challenges that hinder widespread adoption, such as fire safety concerns and the integration of color aesthetics and light-emitting diode technology. This article explores these challenges and recent advancements that enhance the aesthetic versatility and efficiency of BIPV systems, including the use of quantum dots, Bragg reflectors, and silver-alloyed copper indium gallium selenide cells. The study also highlights critical considerations for optimizing BIPV systems, such as energy performance and fire safety. PVsyst 7.2 simulation results for a 10 kW BIPV system installed in South Korea demonstrate effective strategies for mitigating efficiency losses through investigating the orientation of angles. Results showcase a high-performance ratio of 0.891, a high production of 14 147 kWh year−1 at a tilt angle of 30° while facing south, and a peak energy of almost 1450 kWh injected into the grid under varying conditions. © 2025 Wiley-VCH GmbH.

关键词： building-integrated photovoltaics colors efficiencies light-emitting diodes technologies

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PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising

arXiv

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arXiv 2023年

作者： Jang, Hyemi Park, Junsung Jung, Dahuin Lew, Jaihyun Bae, Ho Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Cyber Security Ewha Womans University Korea Republic of

Although supervised image denoising networks have shown remarkable performance on synthesized noisy images, they often fail in practice due to the difference between real and synthesized noise. Since clean-noisy image pairs from the real world are extremely costly to gather, self-supervised learning, which utilizes noisy input itself as a target, has been studied. To prevent a self-supervised denoising model from learning identical mapping, each output pixel should not be influenced by its corresponding input pixel;This requirement is known as J -invariance. Blind-spot networks (BSNs) have been a prevalent choice to ensure J -invariance in self-supervised image denoising. However, constructing variations of BSNs by injecting additional operations such as downsampling can expose blinded information, thereby violating J -invariance. Consequently, convolutions designed specifically for BSNs have been allowed only, limiting architectural flexibility. To overcome this limitation, we propose PUCA, a novel J -invariant U-Net architecture, for self-supervised denoising. PUCA leverages patch-unshuffle/shuffle to dramatically expand receptive fields while maintaining J -invariance and dilated attention blocks (DABs) for global context incorporation. Experimental results demonstrate that PUCA achieves state-of-the-art performance, outperforming existing methods in self-supervised image denoising. Copyright © 2023, The Authors. All rights reserved.

关键词： Image denoising

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Superpixel Tokenization for Vision Transformers: Preserving Semantic Integrity in Visual Tokens

arXiv

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arXiv 2024年

作者： Lew, Jaihyun Jang, Soohyuk Lee, Jaehoon Yoo, Seungryong Kim, Eunji Lee, Saehyung Mok, Jisoo Kim, Siwon Yoon, Sungroh Interdisciplinary Program in AI Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Amazon United States AIIS ASRI INMC ISRC Seoul National University Korea Republic of

Transformers, a groundbreaking architecture proposed for natural language processing (NLP), have also achieved remarkable success in computer vision. A cornerstone of their success lies in the attention mechanism, which models relationships among tokens. While the tokenization process in NLP inherently ensures that each token maintains semantic integrity without containing multiple meanings, the grid-based tokenization of Vision Transformer (ViT) relies on uniformly partitioned square image patches, which may result in an arbitrary mixing of visual concepts within a token. In this work, we propose a novel tokenization pipeline that replaces the grid-based tokenization with superpixels, encouraging each token to capture a distinct visual concept. Unlike square image patches, superpixels are formed in varying shapes, sizes, and locations, making direct substitution challenging. To address this, our pipeline first generates pixel-level embeddings and efficiently aggregates them within superpixel clusters, producing superpixel tokens that seamlessly replace patch tokens in ViT. Extensive experiments demonstrate that our approach enhances the performance of ViT on various downstream tasks and introduces intriguing properties such as adaptive inference and semantic integrity in tokens. © 2024, CC BY.

关键词： Semantics

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How Media Competition Fuels the Spread of Misinformation

arXiv

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arXiv 2024年

作者： Amini, Arash Bayiz, Yigit Ege Lee, Eun-Ju Somer-Topcu, Zeynep Marculescu, Radu Topcu, Ufuk The University of Texas at Austin Oden Institute for Computational Engineering and Sciences United States The University of Texas at Austin Department of Electrical and Computer Engineering United States Seoul National University Department of Communication & Interdisciplinary Program in Artificial Intelligence Korea Republic of The University of Texas at Austin Department of Government United States

Competition among news sources may encourage some sources to share fake news and misinformation to influence the public. While sharing misinformation may lead to a short-term gain in audience engagement, it may damage the reputation of these sources, resulting in a loss of audience. To understand the rationale behind sharing misinformation, we model the competition as a zero-sum sequential game, where each news source influences individuals based on its credibility—how trustworthy the public perceives it—and the individual’s opinion and susceptibility. In this game, news sources can decide whether to share factual information to enhance their credibility or disseminate misinformation for greater immediate attention at the cost of losing credibility. We employ the quantal response equilibrium concept, which accounts for the bounded rationality of human decision-making, allowing for imperfect or probabilistic choices. Our analysis shows that the resulting equilibria for this game reproduce the credibility-bias distribution observed in real-world news sources, with hyper-partisan sources more likely to spread misinformation than centrist ones. It further illustrates that disseminating misinformation can polarize the public. Notably, our model reveals that when one player increases misinformation dissemination, the other player is likely to follow, exacerbating the spread of misinformation. We conclude by discussing potential strategies to mitigate the spread of fake news and promote a more factual and reliable information landscape. Copyright © 2024, The Authors. All rights reserved.

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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Subwavelength-engineered Antislot Photonic Crystals in a Silicon Photonics Foundry for On-chip Communications

Subwavelength-engineered Antislot Photonic Crystals in a Sil...

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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： Arnold, Kellen P. Allen, Joshua A. Halimi, Sami I. Ryder, Landen D. Afzal, Francis O. Bian, Yusheng Aboketaf, Abdelsalam Hirokawa, Takako Dezfulian, Kevin Rakowski, Michal Augur, Rod Nummy, Karen Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University 2201 West End Ave NashvilleTN37235 United States Department of Electrical and Computer Engineering Vanderbilt University 2201 West End Ave NashvilleTN37235 United States NASA Goddard Space Flight Center Code 561 GreenbeltMD20771 United States GlobalFoundries 400 Stone Break Rd. Ext. MaltaNY12020 United States

We report scalable foundry fabrication and characterization of photonic crystal nanobeam waveguides incorporating subwavelength-scale dielectric antislot unit cells. This work enables enhanced light-matter interaction and three-fold improvement in VπL when incorporated in Mach-Zehnder modulators. © Optica Publishing Group 2024

关键词： Silicon photonics

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Subwavelength-engineered Antislot Photonic Crystals in a Silicon Photonics Foundry for On-chip Communications

Subwavelength-engineered Antislot Photonic Crystals in a Sil...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Mach-Zehnder interferometers

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D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

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SN computer Science 2021年第1期2卷 48页

作者： Demiray, Bekir Z. Sit, Muhammed Demir, Ibrahim Department of Computer Science University of Iowa Iowa City United States Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City United States Department of Civil and Environmental Engineering University of Iowa Iowa City United States Department of Electrical and Computer Engineering University of Iowa Iowa City United States

Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network *** study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Deep learning DEM DEM reconstruction Generative adversarial networks (GANs) Super-resolution

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Subwavelength-Engineered Antislot Photonic Crystals in a Silicon Photonics Foundry for On-chip Communications

Subwavelength-Engineered Antislot Photonic Crystals in a Sil...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Kellen P. Arnold Joshua A. Allen Sami I. Halimi Landen D. Ryder Francis O. Afzal Yusheng Bian Abdelsalam Aboketaf Takako Hirokawa Kevin Dezfulian Michal Rakowski Rod Augur Karen Nummy Sharon M. Weiss Interdisciplinary Materials Science Program Vanderbilt University Nashville TN USA Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA NASA Goddard Space Flight Center Greenbelt MD US GlobalFoundries Malta NY USA

ISBN: (纸本)9798350369311

We report scalable foundry fabrication and characterization of photonic crystal nanobeam waveguides incorporating subwavelength-scale dielectric antislot unit cells. This work enables enhanced light-matter interaction and three-fold improvement in $\mathrm{V}_{\pi}\mathrm{L}$ when incorporated in Mach-Zehnder modulators.

关键词： Mach-Zehnder interferometers Optical device fabrication Photonic crystals Waveguide lasers Lasers and electrooptics Electro-optic modulators Silicon photonics Foundries System-on-chip Electro-optical waveguides

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DRL-Based Pricing-Driven for Task Offloading and Dynamic Resource in Vehicle Edge Computing

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IEEE Transactions on Mobile Computing 2025年

作者： Wu, Sijun Yang, Liang Li, Junjie Guo, Hongzhi Ahmad, Ishtiaq Da Costa, Daniel Benevides Jiang, Hongbo Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Shaanxi Xi'an710072 China Czech Technical University in Prague Department of Telecommunication Engineering Faculty of Electrical Engineering Prague166 27 Czech Republic Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore

Vehicle Edge Computing (VEC) assists vehicles in performing latency-sensitive tasks by deploying resources near the vehicle. Designing an incentive mechanism for vehicles and VEC is crucial for realizing an intelligent transmission system. Considering the rationality of resource allocation, we model the utility functions of the VEC and the vehicle, which are used as optimization objectives. Specifically, the VEC allocates resources through pricing to maximize revenue under resource-constrained conditions, and the vehicle weighs payments against energy consumption to determine offloading and resource allocation. Given the vehicle movement and the variable channel state, we use the Deep Reinforcement Learning (DRL) algorithm to solve these optimization problems. To reduce the learning difficulty of the DRL algorithm in complex VEC scenarios with multiple optimization variables, we propose a Pricing-Driven Resource Allocation (PDRA) algorithm that performs mobility-aware task offloading and calculates the optimal values of the optimization variables in the utility function of the vehicle to reduce the decision dimension. Furthermore, we also propose a DRL-based Pricing-Driven Dynamic Resource Allocation (DPDDRA) algorithm to achieve efficient resource allocation. Extensive experimental results show that the proposed algorithms can reduce the learning difficulty while maximizing VEC and vehicle revenue in complex VEC scenarios. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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