Publications

This thesis, titled “Electric Vehicle Policies and Public Reaction: A Public Opinion Analysis Based on Twitter (X) Data,” aims to examine the public’s perceptions, attitudes, and reactions toward Battery Electric Vehicles (BEVs) through social media interactions. With the widespread adoption of Electric Vehicle (EV) technologies, significant changes are taking place in the areas of environmental sustainability, energy policies, and individual consumption behaviors. In this context, the analysis of social media data offers an effective method for understanding public opinion regarding this transformation. In this study, a total of 30,363 tweets were collected from the social media platform Twitter (X) using the Python programming language, the Pandas library, and the Selenium tool. Following data cleaning and preprocessing stages, a final dataset consisting of 28,596 tweets was obtained and used in the analyses. Subsequently, the BERT Sentiment Analysis model for Turkish was applied to classify the posts as positive, negative, or neutral. The analysis revealed that user discourse primarily centered on economic costs, charging infrastructure, domestic production, technological developments, and user experience. It was also determined that topics such as environmental benefits and energy savings featured prominently in user evaluations. The findings indicate that both interest in innovative technologies and economic and infrastructure-related concerns play a decisive role in the public's attitude toward Battery Electric Vehicles (BEVs). As one of the pioneering studies examining public opinion on electric vehicle policies in Turkey using social media data, this research aims to provide a new perspective on sustainable transportation policies.

<jats:p>Early and accurate detection of cracks in structures is crucial for planning structural health and maintenance processes. This study aims to automatically detect cracks on wall and bridge surfaces using deep learning methods with smartphone images. The wall and bridge datasets consist of "cracked" and "crack-free" classes. Eighteen architectures, including convolutional neural networks, transformers, and hybrid architectures, were compared on the local dataset and the SDNET2018 dataset in terms of accuracy, positive predictive value, sensitivity, specificity, F1 score, AUC, and inference time per image. In local experiments, the proposed model achieved 98.50% accuracy on the wall dataset and 100.00% accuracy on the bridge dataset. In contrast, on zero-sample external datasets, accuracy dropped to 52.43%–73.53%. After raw co-training, the SDNET2018 dataset achieved accuracy rates of 86.05%, 81.35%, and 87.21% in wall, bridge deck, and road pavement tests, respectively; in balanced co-training, these values were 82.84%, 80.86%, and 86.57%. The findings show that data-source differences are decisive for model generalization and that multi-domain co-training can improve performance on external datasets. In conclusion, crack detection based on smartphone images can serve as a rapid, low-cost preliminary assessment approach that supports expert inspection in disasters and structural health management.</jats:p>

The effect of temperature, field amplitude, frequency and orientation of the field on AC losses in a commercial Bi:2223/Ag tape prepared by the continuous filling and forming (CTFF) method has been investigated. The measurements were performed using an AC susceptometer. The experimental results show that the losses depend on field amplitude, temperature, DC field, orientation of the sample and strongly on frequency. Due to the strong dependence of AC losses on frequency, measurements were pursued at several frequencies between 20 Hz and 10 kHz at various temperatures (10, 20, 30 and 77 K). The strong frequency dependence at these low temperatures is attributed to eddy current losses and/or inter-filamentary coupling losses. At higher temperatures and low frequencies, the main contribution to AC losses comes from hysteresis losses due to the flux entry into and exit from the superconducting tape during the up and down swings of the magnetic field. We have calculated coupling losses as a function of frequency and temperature-dependent time constant. Experimental data are analyzed by comparing them with the calculated curves. From the DC field dependence of the AC susceptibility, we obtained the irreversibility line.

K-L tabakasi icin bosluk gecis olasiligi (KL) Cr, Fe, Co, Cu, Zn, Ga, Se, Y, Mo, Cd, In, Sn, Te ve Ba gibi bazi saf metaller icin IKβ/IKα siddet orani olcumlerinden elde edildi. Hedef metaller 59,5 keV’lik gama isinlari yayinlayan 50 mCi’lik 241Am radyoaktif kaynak tarafindan uyarildi. Numunelerden yayinlanan karakteristik K X-isinlari 5,96 keV’de 0,150 keV’lik ayirma gucune sahip super Si(Li) detektor kullanilarak belirlendi. Elde edilen deneysel K-L tabakasi bosluk gecis olasilik degerleri ile teorik degerler ile karsilastirildi. Olculen degerlerin teorik degerlerle uyum icinde oldugu gozlendi.

While the newly envisioned smart(er) grid (SG) will result in a more efficient and reliable power grid, its collection and use of fine-grained meter data has widely raised concerns on consumer privacy. While a number of approaches are available for preserving consumer privacy, these approaches are mostly not very practical to be used due to two reasons. 1) Since the data is hidden, this reduces the ability of the utility company to use the data for distribution state estimation. 2) The approaches were not tested under realistic wireless infrastructures that are currently in use. In this paper, we propose to implement a meter data obfuscation approach to preserve consumer privacy that has the ability to perform distribution state estimation. We then assess its performance on a large-scale advanced metering infrastructure (AMI) network built upon the new IEEE 802.11s wireless mesh standard. For the data obfuscation approach, we propose two secure obfuscation value distribution mechanisms on this 802.11s-based wireless mesh network (WMN). Using obfuscation values provided via this approach, the meter readings are obfuscated to protect consumer privacy from eavesdroppers and the utility companies while preserving the utility companies' ability to use the data for state estimation. We assessed the impact of this approach on data goodput, delay, and packet delivery ratio (PDR) under a variety of conditions. Simulation results have shown that the proposed approach can provide very similar performance to that of nonprivacy approach with negligible overheads on the meters and network.

Although it is a deployed system, the data collection process of Smart Grids (SGs) is still a topic that needs consideration. The reason behind this is that Advanced Metering Infrastructure (AMI) applications used for collection can cause leakage of sensitive information about the users if data is sent as plaintext. In this paper, we propose a system that provides privacy with the Paillier cryptosystem and two-factor authentication with ECDSA and OpenSSL certificates. In order to test the system, an IEEE 802.11s-based SG AMI network testbed is constructed with Beaglebone Black boards that imitate the behavior of smart meters. The same network is also simulated in ns-3. Tests are done in two modes (hop-by-hop and end-to-end aggregation) and results are collected based on three metrics (packet delivery ratio, throughput, and completion time). The results showed that ns-3 simulation and testbed results are parallel and the proposed system can perform effectively. However, there are still many differences that need to be taken into account in deploying real systems.

Dağlık, engebeli ve yükseltinin fazla, kar yağışının yoğun olduğu arazilerde birikmiş olan kar tabakasının çeşitli sebeplerle akması çığ olayı olarak ifade edilebilir. Çığlar, can ve mal kayıplarına neden olmanın yanı sıra, çevre üzerinde de olumsuz etkilere yol açar. Ülkemizde ortalama yükseltinin ve eğimli arazilerin fazla olması, ayrıca iklimin etkisi ile kar yağışlarının yer yer yoğun görülmesi çığ olaylarının oluşumunu kolaylaştırmaktadır. Ülkemizde özellikle Doğu Karadeniz Bölümü ve Doğu Anadolu Bölgesi çığ olaylarının fazla görüldüğü sahaları oluşturmaktadır. Bu sahalarda kış döneminde yoğun olarak görülen çığ olaylarında can ve mal kayıpları yaşanmakta, ulaşım yolları kapanmaktadır. Bu nedenle çığ yollarının belirlenmesi ve çığa duyarlı alanlarının tespit edilerek önlem alınması önem taşımaktadır. Bu çalışmanın amacı, büyük kısmı Gümüşhane ili sınırları içinde yer alan Doğankent (Harşit) Çayı Havzası’nın yukarı ve orta kesiminde çığ duyarlılığı yüksek olan alanları belirlemektir. Bu çalışma hazırlanırken Doğankent Çayı Havzası’na ait 1/100.000 ölçekli topografya haritaları sayısallaştırılarak kullanılmıştır. Ayrıca 30x30 boyutlu Aster DEM görüntülerinden de yararlanılmıştır. Bu veriler havzanın eğim, bakı, yükseklik ve yamaç şekli haritaları üretilmesinde altlık verisi olarak kullanılmıştır. Sahanın bitki örtüsü uzaktan algılama ile belirlenmiştir. Çığ duyarlılık haritasının üretilmesinde Afet ve Acil Durum Yönetimi Kurumu'nun puanlama kriterlerinden yararlanılmıştır. Arazi çalışmaları ile çığ yolları ve çığ izleri incelenmiş ve fotoğraflanmıştır. Çığ duyarlılık haritasına göre çığ duyarlılığının çok yüksek olduğu alanlar havzanın güneybatısı ve kuzeydoğusunda yoğunlaşmaktadır. Bu alanlarda çığ duyarlılığının yüksek olmasında dağlık ve engebeli arazi ile bitki örtüsünden yoksun yamaçların yer alması etkilidir. Ayrıca önemli bir ulaşım yolu olan Gümüşhane-Trabzon karayolunun bulunduğu sahalarda eğim değerlerinin yüksek olması çığ duyarlılığını yükseltmektedir. Buna karşın çığ duyarlılığının düşük olduğu yerler havzanın kuzeybatı kesimidir. Buralardaki duyarlılığının düşük olmasında gür orman örtüsü ve dikili arazilerin (fındık vb. gibi) varlığının etkili olduğu düşünülmektedir. Çalışma sonucunda elde edilen duyarlılık haritası ile havzada çığ olayları için alınabilecek önlemler birlikte değerlendirilmiştir. Çığ yolları ve kurulması gereken çığ güvenlik setlerinin konumları arazide tespit edilmiştir. Dolayısıyla bu çalışmanın, yaşanabilecek çığ olaylarına karşı yetkili mercilerin tedbir almasına dönük yararlı bir kaynak olacağı düşünülmektedir.

One of the most common methods to preserve consumers' private data is using secure in-network data aggregation. The security can be provided through the emerging fully (FHE) or partial (PHE) homomorphic encryption techniques. However, an FHE aggregation scheme generates significantly big-size data when compared to traditional encryption methods. The overhead is compounded in hierarchical networks such as Smart Grid Advanced Metering Infrastructure (AMI) as data packets are routed towards the core of the AMI networking infrastructure from the smart meters. In this paper, we first investigate the feasibility and performance of FHE aggregation in AMI networks utilizing the reliable data transport protocol, TCP. Then, we introduce the packet reassembly problem. To address this challenge, we propose a novel packet reassembly mechanism for TCP. We evaluated the effectiveness of our proposed mechanism using both PHE and FHE-based aggregation approaches in AMI in terms throughput and end-to-end delay on an 802.11s-based wireless mesh network by using the ns-3 network simulator. The results indicate significant gains in terms of delay and bandwidth usage with the proposed mechanism.