Maximization of Solar Radiation for Fixed and Tracking Surfaces in Antalya Province of Türkiye

Solar energy has gained increasing importance in today\u27s world and become a viable primary energy source in the recent decade. Solar radiation obtained by the solar surface is highly affected by its orientation, azimuth, and tilt angles. Therefore, in this study, the performances of the fixed-axis system, one-axis, and two-axis solar tracking systems are investigated to enable maximal solar radiation in solar systems to be installed in Antalya by using climatic and latitude data provided by NASA. Furthermore, the optimal tilt angles are determined by examining the values of angles for which the total solar radiation falling on the tilted surface is maximal. The case study and measurement data investigations are conducted for the four districts of Antalya. The obtained radiation values throughout the year for one-axis and two-axis solar tracking systems are compared to an annual fixed system for evaluating the existing solar potential in Antalya province. Besides all these, solar system cost analyses including the average payback period for residential, commercial, and large-scale solar systems based on LCOE are investigated. The proposed methodology can be implemented for performance and cost analysis of the solar potential in a certain location of Türkiye and extended to any place in the world

Silikon Atomunun X-ışını Bölgesinde Foto Soğurma Tesir Kesitinin Hesaplanması

Astrofizik uygulamalarda, yüksek-çözünürlüklü X-ışını spektroskopinin asıl amacı uzaydan gelen ışınlarda yer alan soğurma çizgilerinin tespiti ve tespit edilen çizgilerin özelliklerinin modellemeler kullanarak belirlemektir. X-ışını bölgesindeki gözlemlenen soğurma çizgilerinin kaynağının büyük bir çoğunluğu atomların iç kabuklarındaki foton elektron etkileşmesidir. Spektroskopi gözlemlerinin modellenmeleri ve yorumlanabilmeleri soğurmaya sebep olan atomlara ait foto soğurma tesir kesitlerinin ulaşılabilir olmasına bağlıdır. Bu tür gözlemlerin modellenmesinde sıklıkla başvurulan kapsamlı çalışma Serbest-Parçacık yaklaşımı çerçevesinde yapılmıştır. Bu tesir kesitleri geçmişte elde edilen düşük-çözünürlüğe sahip gözlemlerin modellenmesi amacı için yeterli olmuştur. Serbest-Parçacık yaklaşımı ile hesaplanmış tesir kesitlerini kullanan X-ışını soğurma modelleri, Chandra X-Ray Observatory ve Avrupa Uzay Ajansına ait X-ray Multi-Mirror Mission (XMM-Newton) uydu teleskoplarının sunmuş olduğu yüksek çözünürlüğe sahip yüksek-çözünürlüklü spektroskopi gözlemleri için yeterli değillerdir. Bunun sebebi bu tesir kesitleri yüksek çözünürlüklü spektroskopi için ayrıca büyük önemli olan 1s-np geçişlerine ait soğurma etkilerini içermemeleridir. X-ışını bölgesinde atomların tesir kesitleri deneysel olarak istenilen seviyede yüksek çözünürlüklü ölçümleri çok zordur. X-ışını bölgesinde silikon atomunun istenilen nitelikte tesir kesiti için deneysel bir ölçüm veya herhangi bir teorik çalışma literatürde mevcut değildir. Bu projede silikon atomunun X-ışın bölgesinde foto soğurma tesir kesitini son-teknoloji Rmatrix metodu kullanılarak teorik olarak hesaplanmıştır. R-matrix metodu daha önce oksijen, neon, karbon ve magnezyum atomların için tesir kesitlerinin hesaplanmasında kullanılmış ve bu sonuçlar astrofizik modellemelerde kullanılmıştır. Ayrıca bu çalışmalardan elde edilen tesir kesitleri astrofizik modellemelerinde kullanılan XSTAR, CLOUDY ve ION gibi önemli X-ışını spektroskopi modelleme programlarına entegre edilmişlerdir. Astrofizik modellemede Rydberg rezonanslarına ait soğurma tesir kesitlerinin hassasiyetle bilinmesi ayrıca önemlidir. Bu rezonansların genişliklerinin doğru şekilde tayin edilmesi için büyük öneme arz eden İzleyici Auger Genişleme etkileri hesaba dâhil edilmişlerdir. Yakın zamanda silikon atomunun K-kabuğu soğurma bölgesine karşılık gelen astrofizik modelleme çalışmaları silikon atomunun kristal yapıda toz parçacıkları halinde var olduğu bulunmuştur. Silikonun atom halinde var olup olmadığı konusundaki yapılacak astrofizik modellemelerde bu projeden elde edilen sonuçların kullanılması ile mümkündür

PIC-Based Solar Energy Harvesting Module Design for Wireless Sensor Networks

Günümüzde, kablosuz sensör ağlarında (WSN'ler) güç tüketimi konusu çok önemlidir ve songelişmeler, kablosuz sensör ağları için güneş enerjisi gücünün kullanılmasını sağlamıştır.WSN'ler için güneş enerjisinin verimli bir şekilde nasıl toplanıp depolanacağı bu çalışmadaaraştırılmıştır. WSN'lerde enerji tüketimi sorunları, kablosuz sensör düğümlerinin en belirginkısıtlamalarıdır. Güç sınırlamasına bağlı olarak, WSN'lere sürekli olarak elektrik gücü sağlamakçoğu zaman çok zor olmaktadır. Bu sorunların üstesinden gelmek üzere, kablosuz sensördüğümlerini şarj edilebilir pillerle birlikte, güneş enerjisi gibi alternatif-yenilenebilir enerjikaynakları ile güçlendirmeye odaklanılmıştır. Ayrıca, enerji tasarrufu için farklı tekniklerkullanılması WSN'lerde önemli işlerden biridir. Donanım, yazılım, iletişim protokolleri veuygulamaların enerji tüketimini azaltmak üzere birçok çaba sarf edilmiştir. Bununla birlikte,WSN’lerde enerji tüketimi halen istenilen seviyeye düşürülememiştir. Bu nedenle, WSN'lerin güçtüketimi sorununu çözmek için yenilenebilir-hasat enerji yaklaşımı ve çevresel uygulamalardaWSN'lerin ömrünü artırmak için yenilenebilir (güneş enerjisi) enerjinin kullanımı yaklaşımısunulmuştur. Bu çalışmada ayrıca, herhangi bir WSN'nin hızlı prototipleştirilmesi için yeni birenerji toplama ve tasarruf etme modeli önerilmiştir. Bu yaklaşımların bir kablosuz sensör ağınınömrünün artırılmasında kritik bir rol oynayabileceği bulunmuştur.Nowadays, the issue of power consumption is very important in wireless sensor networks (WSNs) and recent developments have allowed solar energy power to be used for WSNs. How to collect and store energy effectively from the environment has been investigated for the WSNs in this paper. Energy consumption issues in theWSNs is the most significant constraints of the wireless sensor node systems. Due to the power limitation, providing electrical power to WSNs often becomes very challenging.Overcome these issues, we have focused on powering up the sensor devices by alternative renewable energy sources such as solar energy along with rechargeable batteries. Also, using different techniques for energy saving is one of the essential tasks in WSNs. Many efforts have been put to reduce the energy consumption of the hardware, software, communication protocols an applications. However, energy consumption still cannot be brought down to the desiredlevel. Therefore,we have presenteda renewable-harvesting energy approach for solving the power consumption problem of the WSNs andthe utilization of renewable (solar) energy to enhance the lifespan of the WSNs in environmental applications. In thıs study, we have also proposed a new renewable powered energy harvesting model for the rapid prototyping of any WSN. We have found that these approaches may play a critical role in increasingthe lifespan of a WSN</p

Investigation and Implementation Ultra-Low Power PIC Based Sensor Node Network with Renewable Energy Source and Decision-Making Unit

Advancing technology has enabled the production of smaller, more energy efficient and cheaper electronic components. Therefore, previously existing many computer and electronics science-engineering ideas have become feasible. One of them is the technology of wireless sensor networks (WSNs), which has become the realization of the necessary technical requirements applicable today with low energy consumption. First, the sensing tasks and the potential sensor networks applications have explored, and reviews of factors influencing the design of sensor networks have provided. Then, the communication architectures for sensor networks have been outlined. PIC-based microcontrollers have used in the design of the sensor nodes. The design of the sensor node has supported with ultra-low power nanowatt technology for very low-cost design. Processing, memory and wireless communication units have integrated on to the sensor nodes and sensors to be used in the designed system which have allowed to be connected to any kind of sensor node. The designed sensor node’s operating system has written with the PIC C language, and PIC operating system has allowed different features such as measuring humidity, temperature, light sensitive and smoke sensor. Computer software has developed for data that can be recorded and monitored from a central location. Decision-making unit has created in the software algorithm and hardware modules for the implementation of decisions taken by the developed sensor nodes. Developed PIC-based sensor nodes have supported a unique voltage unit with renewable energy sources such as solar panel, rechargeable battery, and supercapacitor for energy production and saving. The results of this study are expected to be helpful for the development of WSN especially with renewable energy sources.</p

Plant Disease Detection by Using Adaptive Neuro-Fuzzy Inference System

This study aims the detection and recognition of plant diseases by using the most modern methods including Support Vector Machine (SVM), Convolution Neural Network (CNN), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Interface System (ANFIS). In the studies, 2340 training and 585 test data were used with 3 different tomato plant leaves as Healthy, Early blight, and Yellow leaf curl virus. These methods are used in a wide spectrum of research areas. While creating the dataset, a total of 11 features were extracted from the existing image data. 91% accuracy has been achieved with the proposed ANFIS which is the best compared to the other methods with 11 features.</p

Investigation and determination of optimal tilt angles and solar radiation gains for fixed and tracked south-facing solar photovoltaic surfaces in provinces of türkiye

The solar photovoltaic (PV) plants in Türkiye have been advancing at a remarkable rate in the last decades because of the region’s high solar energy potential. However, it is understood from the literature review that there are still limited research works on the optimization of the tilt angles of PV surfaces to maximize the solar radiation of the PV energy systems in this region. Therefore, this study focuses on a quantitative analysis of the optimal tilt angles of south-oriented PV surfaces and the amount of solar radiation collected by optimally tilted and tracked PV surfaces for all provinces by using the National Aeronautics and Space Administration-Surface meteorology and Solar Energy (NASA-SSE) horizontal radiation data for the provinces in Türkiye. Also, a numerical method is proposed to estimate the average daily solar radiation values falling on optimally tilted and tracked PV surfaces in 81 provinces and 7 geographical regions of Türkiye in this paper. An optimal tilt angles map has been created for all provinces where solar plants could be established. Solar data calculations have been carried out for all provinces, and the results are presented for the average total radiation amounts and percentage contributions that can be obtained in the case of installing fixed systems and tracking systems. It is found that the tilt angles of all provinces and regions are below the latitude values of Türkiye (36°′N–42°′N). Annual fixed optimal tilt angle values for south-facing PV surfaces are found between 28° and 36° throughout the year. It is observed that the daily average total radiation values falling on PV surfaces are considerably affected by the geographical and climatic characteristics even between the provinces with close latitude values in Türkiye. The results indicate that the tracking systems provide remarkable solar energy gains compared to the annual fixed systems. The proposed methodology can be used in the case of the implementation of a large-scale PV plant in any location in Türkiye, and this knowledge can be extended to the world thanks to low computational cost. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG

Development of a Real-Time Traffic Sign Recognition System Based On Deep Learning Approach with Convolutional Neural Networks and Integrating to The Embedded Systems

Traffic signs are mandatory features of road traffic regulations worldwide. Automatic detection and recognition of traffic signs by vehicles may increase the safety level of drivers and passengers. For this reason, Real Time-Traffic Sign Recognition (RT-TSR) system is one of the essential components for smart transportation systems and high-tech vehicles. Recently, very good performances have been achieved in public datasets, especially with advanced Computer Vision (CV) approaches like Deep Learning (DL). Nevertheless, these CV techniques still need to be improved to provide the requirements of Real-Time (RT) applications. Although hopefully outcomes have been obtained theoretically in previous Traffic Sign Recognition (TSR) studies, there are very few studies that offer RT solutions in the real world. Therefore, in this study, a DL-based RT-TSR system is developed because of its high rate of recognition and quick execution. Besides, the CV approach has been included in the software developed to achieve classification as RT and support digital imaging. This developed system is capable of running smoothly in embedded systems with mobile GPU or CPU thanks to its low computational cost and high performance. Therefore, this study makes two important contributions to this field: software and hardware. First, RT-TSR software has been developed by using Convolutional Neural Networks (CNN) built on DL techniques along with CV techniques. Secondly, the developed software is adapted to embedded devices and hardware design is made. This developed system is also a technology product that offers software and hardware solutions together. Coding is accomplished under TensorFlow and OpenCV frameworks with the python programming language and CNN training is carried out by using parallel architecture. The experimental findings indicate that the developed CNN architecture achieves 99,71% accuracy and confirms the high efficiency of the system.</p

Development of a Real-Time Traffic Sign Recognition System Based on Deep Learning Approach With Convolutional Neural Networks and Integrating to the Embedded Systems

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L -shell photoionization of Mg-like S4+ in ground and metastable states: Experiment and theory

We report measurements of the absolute photoionization cross sections of magnesiumlike S4+ over the 158-280 eV photon energy range. The experiments were performed with the multianalysis ion apparatus at the SOLEIL synchrotron radiation facility. Single- and double-ionization ion yields produced by the photoionization of the 2p subshell of the S4+ both from the 2p63s21S0 ground state and the 2p53s3p3P0,1,2 metastable levels were observed, as well as 2s excitations. Theoretical calculations of the photoionization cross sections were carried out using multiconfiguration Dirac-Fock and R-matrix computer codes and the results are compared with the experimental data. While in general reasonably good agreement was found, notable differences in the strengths and positions of predicted resonances were observed and significant systematic energy shifts of the theoretical predictions were required. © 2022 American Physical Society