Sakarya University Journal of Science最新文献_第3页

Synthesis and Characterization of Novel Water-Soluble Tetra-Substituted Zn(II) Phthalocyanine Containing Triazole and Galactose Moieties 新型水溶性含三唑半乳糖四取代酞菁锌的合成与表征

Sakarya University Journal of Science Pub Date : 2023-08-25 DOI: 10.16984/saufenbilder.1266799

Yasemin BAYĞU

引用次数: 0

Transforming Sketches into Realistic Images: Leveraging Machine Learning and Image Processing for Enhanced Architectural Visualization 将草图转换为逼真的图像:利用机器学习和图像处理增强建筑可视化

Sakarya University Journal of Science Pub Date : 2023-08-07 DOI: 10.16984/saufenbilder.1319166

I. Karadag

{"title":"Transforming Sketches into Realistic Images: Leveraging Machine Learning and Image Processing for Enhanced Architectural Visualization","authors":"I. Karadag","doi":"10.16984/saufenbilder.1319166","DOIUrl":"https://doi.org/10.16984/saufenbilder.1319166","url":null,"abstract":"This article presents a novel approach for transforming architectural sketches into realistic images through the utilization of machine learning and image processing techniques. The proposed method leverages the Stable Diffusion model, a deep learning framework specifically designed for text-to-image generation. By integrating image processing algorithms into the workflow, the model gains a better understanding of the input sketches, resulting in visually coherent and meaningful output images. The study explores the application of the Stable Diffusion model in the context of architectural design, showcasing its potential to enhance the visualization process and support designers in generating accurate and compelling representations. The efficacy of the method is evaluated through qualitative and quantitative assessments, demonstrating its effectiveness in bridging the gap between initial sketches and photorealistic renderings. This research contributes to the growing body of knowledge on the integration of machine learning and image processing in architecture, providing insights and practical implications for design professionals and researchers in the field.","PeriodicalId":21468,"journal":{"name":"Sakarya University Journal of Science","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83764236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning Techniques 基于深度学习技术的电力系统短期负荷预测

Sakarya University Journal of Science Pub Date : 2023-08-03 DOI: 10.16984/saufenbilder.1256743

Nihat PAMUK

{"title":"Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning Techniques","authors":"Nihat PAMUK","doi":"10.16984/saufenbilder.1256743","DOIUrl":"https://doi.org/10.16984/saufenbilder.1256743","url":null,"abstract":"The use of big data in deep neural networks has recently surpassed traditional machine learning techniques in many application areas. The main reasons for the use of deep neural networks are the increase in computational power made possible by graphics processing units and tensor processing units, and the new algorithms created by recurrent neural networks and CNNs. In addition to traditional machine learning methods, deep neural networks have applications in anticipating electricity load. Using a real dataset for one-step forecasting, this article compares three deep learning algorithms for short-term power load forecasting: LSTM, GRUs, and CNN. The statistics come from the Turkish city of Zonguldak and include hourly electricity usage loads and temperatures over a period of three years, commencing in 2019 and ending in 2021. The mean absolute percentage error is used to compare the performances of the techniques. Forecasts are made for twelve representative months from each season. The main reason for the significant deviations in the forecasts for January, May, September, and December is the presence of religious and national holidays in these months. This was solved by adding the information obtained from religious and national holidays to the modeling. This is not to say that CNNs are not good at capturing long-term dependencies and modeling sequential data. In all experiments, LSTM, GRUs and encoder-decoder LSTM outperformed simple CNN designs. In the future, these new architectural methods can be applied to long- or short-term electric charge predictions and their results can be compared to LSTM, GRUs and their variations.","PeriodicalId":21468,"journal":{"name":"Sakarya University Journal of Science","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136382697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sturm-Liouville Problems with Polynomially Eigenparameter Dependent Boundary Conditions 具有多项式特征参数相关边界条件的 Sturm-Liouville 问题

Sakarya University Journal of Science Pub Date : 2023-07-31 DOI: 10.16984/saufenbilder.1304365

A. Kabataş

引用次数: 0

One-step and Cost-effective Conversion of Polyimide to Graphene by Utilizing a Desktop Laser 利用台式激光器一步高效地将聚酰亚胺转化为石墨烯

Sakarya University Journal of Science Pub Date : 2023-07-30 DOI: 10.16984/saufenbilder.1201851

Nihan AYDEMİR

引用次数: 0

An endemic vascular plant species for Turkey, Ferulago humilis Boiss., and its potential distribution areas 土耳其特有维管束植物物种 Ferulago humilis Boiss 及其潜在分布区

Sakarya University Journal of Science Pub Date : 2023-07-30 DOI: 10.16984/saufenbilder.1302376

Ece Gökçe ÇAKIR-DİNDAR, Behlül Güler

{"title":"An endemic vascular plant species for Turkey, Ferulago humilis Boiss., and its potential distribution areas","authors":"Ece Gökçe ÇAKIR-DİNDAR, Behlül Güler","doi":"10.16984/saufenbilder.1302376","DOIUrl":"https://doi.org/10.16984/saufenbilder.1302376","url":null,"abstract":"Turkey is expected to be affected considerably in future adverse climatic conditions. Plant species are one of the most vulnerable to these climatic changes. In this study, we aimed to investigate current and future potential distribution areas of Ferulago humilis Boiss., which is an endemic vascular plant species for Turkey, using CMIP5 projected to year 2070. For this purpose, we obtained occurence data (presence-only) from Global Biodiversity Information Facility (GBIF). Regarding bioclimatic data we used WorldClim dataset with 10 km2 resolution. Using both plant occurrence and bioclimatic data, we performed species distribution modelling analysis. We used two methods namely Boosted Regression Trees (BRT) and Random Forest (RF). Additionally, we used bootstrapping method as partitioning resampling for all analysis. Our analysis has showed that potential distribution areas of the species has slightly changed for the future projection. The species movement is towards slightly upwards as higher latitudes. We believe that our study shows the importance and relevance of the endemic species in the scope of species distribution models for plant conservation topics.","PeriodicalId":21468,"journal":{"name":"Sakarya University Journal of Science","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139353864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis and Control of Chaos in Permanent Magnet Synchronous Motor 永磁同步电机中的混沌分析与控制

Sakarya University Journal of Science Pub Date : 2023-07-27 DOI: 10.16984/saufenbilder.1286774

Abdallah MOUSSA YAYA, Y. Uyaroğlu

引用次数: 0

Common Solutions to Stein Inequalities 斯坦因不等式的常见解法

Sakarya University Journal of Science Pub Date : 2023-07-25 DOI: 10.16984/saufenbilder.1260438

Şerife Yilmaz, Birgül Aksoy

引用次数: 0

Investigation of the Usage Properties of Steel Slag as Pigment in Low-Temperature Glaze 钢渣作为颜料在低温釉中的使用性能研究

Sakarya University Journal of Science Pub Date : 2023-07-22 DOI: 10.16984/saufenbilder.1264392

Muhterem Koç, E. Taşçi

引用次数: 0

Estimating Human Poses Using Deep Learning Models 使用深度学习模型估计人体姿势

Sakarya University Journal of Science Pub Date : 2023-07-17 DOI: 10.16984/saufenbilder.1311198

Fırgat Muradli, Serap Cakar, Feyza Cerezci, Guluzar Cit

引用次数: 0