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Into 3D-Reconstructed Apparel over Virtual Try-On with Virtual Reality

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Into 3D-Reconstructed Apparel over Virtual Try-On with Virtu...

IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Surasachai Doungtap Jirayu Petchhan Varinya Phanichraksaphong Jenq-Haur Wang International Graduate Program of Electrical Engineering and Computer Science Dept. of Electrical Engineering National Taiwan University of Science & Technology Taiwan R. O. C. Department of Computer Science and Information Engineering National Taipei University of Technology Taiwan R. O. C.

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-depth utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle.

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Investigation of availability,demand,targets,and development of renewable energy in 2017-2050:a case study in Indonesia

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International Journal of Coal Science & Technology 2021年第4期8卷 483-499页

作者： Erdiwansyah Erdiwansyah Mahidin Mahidin Husni Husin Nasaruddin Nasaruddin Khairil Khairil Muhammad Zaki Jalaluddin Jalaluddin Doctoral Program School of EngineeringUniversitas Syiah KualaBanda Aceh 23111Indonesia Department of Chemical Engineering Universitas Syiah KualaBanda Aceh 23111Indonesia Department of Electrical and Computer Engineering Syiah Kuala UniversityBanda Aceh 23111Indonesia Department of Mechanical Engineering Universitas Syiah KualaBanda Aceh 23111Indonesia Department of Accounting Universitas Syiah KualaBanda Aceh 23111Indonesia Faculty of Engineering Universitas Serambi MekkahBanda Aceh 23245Indonesia

Abundant potential of renewable energy(RE)in Indonesia is predicted to replace conventional energy which continues to experience depletion year by ***,until now,the use of RE has only reached 2%of the existing potential of 441.7 *** main overview of this work is to investigate the availability of RE that can be utilized for electricity generation in *** energy demand and targets in the long run during the 2017-2050 period are also ***,government policies in supporting RE development are considered in this *** results show that the potential of RE in Indonesia can be utilized and might replace conventional energy for *** use of RE for electricity generation can be achieved by employing a government policy that supports the investor as the executor of RE *** selling price of electricity generated from RE is cheaper than electricity generated from fossils;this makes economy is more affordable for ***,the target set by the government for utilizing RE as the main energy in Indonesia can be done by implementing several policies for the RE ***,greenhouse gas emissions and the use of petroleum fuels can be reduced.

关键词： Energy potential Energy demand Energy development Economic growth Energy policy Energy targets

CleftGAN: Adapting A Style-Based Generative Adversarial Network To Create Images Depicting Cleft Lip Deformity

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arXiv 2023年

作者： Hayajneh, Abdullah Serpedin, Erchin Shaqfeh, Mohammad Glass, Graeme Stotland, Mitchell A. Electrical and Computer Engineering Department Texas A&M University College StationTX United States Electrical and Computer Engineering Program Texas A&M University Doha Qatar Division of Plastic Craniofacial and Hand Surgery Sidra Medicine Weill Cornell Medical College Doha Qatar

A major obstacle when attempting to train a machine learning system to evaluate facial clefts is the scarcity of large datasets of high-quality, ethics board-approved patient images. In response, we have built a deep learning-based cleft lip generator designed to produce an almost unlimited number of artificial images exhibiting high-fidelity facsimiles of cleft lip with wide variation. We undertook a transfer learning protocol testing different versions of StyleGAN-ADA (a generative adversarial network image generator incorporating adaptive data augmentation (ADA)) as the base model. Training images depicting a variety of cleft deformities were pre-processed to adjust for rotation, scaling, color adjustment and background blurring. The ADA modification of the primary algorithm permitted construction of our new generative model while requiring input of a relatively small number of training images. Adversarial training was carried out using 514 unique frontal photographs of cleft-affected faces to adapt a pre-trained model based on 70,000 normal faces. The Frechet Inception Distance (FID) was used to measure the similarity of the newly generated facial images to the cleft training dataset, while Perceptual Path Length (PPL) and the novel Divergence Index of Severity Histograms (DISH) measures were also used to assess the performance of the image generator that we dub CleftGAN. CleftGAN demonstrates an ability to automatically generate vast numbers of unique faces depicting a wide range of cleft lip deformity including a variation of ethnic/racial background. We found that StyleGAN3 with translation invariance (StyleGAN3-t) performed optimally as a base model. Generated images achieved a low FID reflecting a close similarity to our training input dataset of genuine cleft images. Low PPL and DISH measures reflected a smooth and semantically valid interpolation of images through the transfer learning process and a similar distribution of severity in the training and gen

关键词： Generative adversarial networks

Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics

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Novel NBA Fantasy League driven by Engineered Team Chemistry...

IEEE International Conference on Big Data

作者： Ganesh Arkanath Nishad Gupta Hasan Kurban Parichit Sharma K R Madhavan Elham Khorasani Buxton Mehmet M. Dalkilic Computer Science Department Indiana University Bloomington IN US Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Data Science Program Indiana University Bloomington IN US Computer Science Department University of Illinois Springfield IL US

Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position; (2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA’s API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://***/fantasyleague/, and the source code can be found at https://***/gany-15/nbafan.

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Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin Poor, H. Vincent The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China 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 Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Driven by advances in generative artificial intelligence (AI) techniques and algorithms, the widespread adoption of AI-generated content (AIGC) has emerged, allowing for the generation of diverse and high-quality content. Especially, the diffusion model-based AIGC technique has been widely used to generate content in a variety of modalities. However, the real-world implementation of AIGC models, particularly on resource-constrained devices such as mobile phones, introduces significant challenges related to energy consumption and privacy concerns. To further promote the realization of ubiquitous AIGC services, we propose a novel collaborative distributed diffusion-based AIGC framework. By capitalizing on collaboration among devices in wireless networks, the proposed framework facilitates the efficient execution of AIGC tasks, optimizing edge computation resource utilization. Furthermore, we examine the practical implementation of the denoising steps on mobile phones, the impact of the proposed approach on the wireless network-aided AIGC landscape, and the future opportunities associated with its real-world integration. The contributions of this paper not only offer a promising solution to the existing limitations of AIGC services but also pave the way for future research in device collaboration, resource optimization, and the seamless delivery of AIGC services across various devices. Our code is available at https://***/HongyangDu/DistributedDiffusion. © 2023, CC BY.

关键词： Wireless networks

Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP

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arXiv 2024年

作者： Kim, Eunji Shim, Kyuhong Chang, Simyung Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Qualcomm AI Research Qualcomm Korea YH Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

A text encoder within Vision-Language Models (VLMs) like CLIP plays a crucial role in translating textual input into an embedding space shared with images, thereby facilitating the interpretative analysis of vision tasks through natural language. Despite the varying significance of different textual elements within a sentence depending on the context, efforts to account for variation of importance in constructing text embeddings have been lacking. We propose a framework of Semantic Token Reweighting to build Interpretable text embeddings (SToRI), which incorporates controllability as well. SToRI refines the text encoding process in CLIP by differentially weighting semantic elements based on contextual importance, enabling finer control over emphasis responsive to data-driven insights and user preferences. The efficacy of SToRI is demonstrated through comprehensive experiments on few-shot image classification and image retrieval tailored to user preferences. © 2024, CC BY-NC-SA.

关键词： Embeddings

High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor 16

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High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Park, Nu-Ri Kim, Min-Joong Lee, Jongsu Seo, Minah Ryu, Yong-Sang Kim, Myung-Ki KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Korea Institute of Science and Technology Sensor System Research Center Seoul02792 Korea Republic of Northwestern University Department of Electrical Engineering and Computer Science EvanstonIL60208 United States School of Biomedical Engineering Korea University Seoul02481 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02481 Korea Republic of

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen ( ©... 详细信息

ISBN: (纸本)9798350372076

关键词： Nanoclay

The Influence of Overcharging and Over-Discharging on the Capacity Degradation of Lithium-Ion Batteries

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The Influence of Overcharging and Over-Discharging on the Ca...

IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Joelton Deonei Gotz Marco Antonio Simões Teixeira Fernanda Cristina Corrêa Emilson Ribeiro Viana Alceu André Badin Milton Borsato Graduate Program in Electrical and Computer Engineering Federal Technological University of Paraná (UTFPR-CT) Curitiba Brazil Graduate Program in Informatics (PPGIa) Pontifical Catholic University of Paraná (PUC-PR) Curitiba Brazil Graduate Program in Electrical Engineering Federal Technological University of Paraná (UTFPR-PG) Ponta Grossa Brazil Physics Department Graduate Program in Physics and Astronomy Federal Technological University of Paraná (UTFPR-CT) Curitiba Brazil Graduate Program in Mechanical Engineering Federal Technological University of Paraná (UTFPR-CT) Curitiba Brazil

ISBN: (数字)9798331541606

ISBN: (纸本)9798331541613

Lithium-ion batteries (LIB) are the best technology for supplying and storing energy for electric mobility systems. Despite that, this technology is sensitive to abuses that can compromise its lifetime and cause fire and explosion risks. In this context, if LIB is not correctly controlled, some abuses, such as overcharging (OC) and over-discharging (OD), can be observed and provoke a loss of capacity. Therefore, this work presented the application of several experiments in cells, of the NMC type, under OC and the combination of OC and OD. The results indicated that cells charged until 4.4 and 4.5 V and discharged until 3.0 V do not lose capacity for twenty-five cycles, but more cycles should be done to understand the impact over time. On the other side, the cell charged until 4.6 and 4.8 V had its security protection romped in the fourth charging cycle, and the cell charged until 5.0 V had its safety compromised in the first charging cycle. Conversely, cells with a combination of OC and OD were more compromised. In this context, while a cell is submitted only with OC and does not lose capacity, a cell under OC and then under OD had its protection romped in the third charging cycle. Cells under 4.6 V and 4.8 V lost capacity during the second charging cycle. In conclusion, OC provokes a high level of degradation in the cell, but the combination of OC and OD causes even more degradation.

关键词： Degradation Lithium-ion batteries Battery management systems Propulsion Security Reliability Protection Stress Monitoring Predictive maintenance

INTERPRETABLE LANGUAGE MODELING VIA INDUCTION-HEAD NGRAM MODELS

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arXiv 2024年

作者： Kim, Eunji Mantena, Sriya Yang, Weiwei Singh, Chandan Yoon, Sungroh Gao, Jianfeng Microsoft Research United States Department of Electrical and Computer Engineering Seoul National University Korea Republic of Stanford University United States Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

Recent large language models (LLMs) have excelled across a wide range of tasks, but their use in high-stakes and compute-limited settings has intensified the demand for interpretability and efficiency. We address this need by proposing Induction-head ngram models (Induction-Gram), a method that builds an efficient, interpretable LM by bolstering modern ngram models with a hand-engineered "induction head". This induction head uses a custom neural similarity metric to efficiently search the model’s input context for potential next-word completions. This process enables Induction-Gram to provide ngram-level grounding for each generated token. Moreover, experiments show that this simple method significantly improves next-word prediction over baseline interpretable models (up to 26%p) and can be used to speed up LLM inference for large models through speculative decoding. We further study Induction-Gram in a natural-language neuroscience setting, where the goal is to predict the next fMRI response in a sequence. It again provides a significant improvement over interpretable models (20% relative increase in the correlation of predicted fMRI responses), potentially enabling deeper scientific investigation of language selectivity in the brain. The code is available at https://***/ejkim47/induction-gram. © 2024, CC BY-NC-SA.

关键词： Digital elevation model