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MAKROEKONOMİK PERFORMANS VE ENTEGRE CRITIC TABANLI MABAC KARAR VERME YAKLAŞIMI: TÜRKİYE EKONOMİSİNİN 2008-2021 DÖNEM VERİSİNDEN KANITLAR
No full textBu çalışmada entegre CRITIC tabanlı MABAC yaklaşımı ile Türkiye’nin 2008-2021 dönemi makroekonomik performansının değerlendirilmesi amaçlanmıştır. Gelişmiş, gelişmekte olan ve az gelişmiş olan ülkelerin makroekonomik performansının değerlendirilmesinde birçok kriter kullanılmaktadır. Burada ekonomik büyüme, yatırım oranı, ihracat oranı, ithalat oranı, cari işlemler dengesi oranı, işsizlik oranı, enflasyon oranı ve faiz oranı gibi kriterler dikkate alınarak Türkiye’nin makroekonomik performansı analiz edilmiştir. Çalışmada ihracat oranı kriterinin en yüksek önem düzeyine sahip kriter olduğu tespit edilmiştir. İhracatın artması ile dengeli döviz kuru politikasının oluşabileceği ve dış ticaret açığının kapanabileceği beklenmektedir. Küresel finans krizinin yaşandığı 2008 yılında Türkiye en düşük makroekonomik performansı elde ederken, 2015 yılında ise en yüksek ekonomik performansı gerçekleştirdiği belirlenmiştir. Kronik enflasyon, kur şokları, döviz rezerv yetersizliği ile borçlanma maliyetlerinin yüksekliği gibi birçok faktörün etkisiyle ilgili dönemde istikrarlı olmayan bir ekonomik performansın ortaya çıktığı anlaşılmaktadır
Network anomaly detection with a hybrid approach of machine learning algorthms
No full textİÇİNDEKİLER TEŞEKKÜR ................................................................................................................ i İÇİNDEKİLER .......................................................................................................... ii KISALTMALAR ...................................................................................................... iv TABLOLAR LİSTESİ.............................................................................................. vi ŞEKİLLER LİSTESİ............................................................................................... vii ÖZET........................................................................................................................viii ABSTRACT............................................................................................................... ix BÖLÜM 1. GİRİŞ .......................................................................................................................... 1 1.1. Ağ Anomali Tespiti.......................................................................................... 2 1.2. Ağ Saldırıları.................................................................................................... 3 1.3. Veri Kümeleri................................................................................................... 7 1.1. NSL- KDD..................................................................................................... 8 1.2. UNSW-NB15................................................................................................. 8 1.3. CICIDS2017 .................................................................................................. 9 1.4. DARPA 1998............................................................................................... 10 1.5. KDDCUP99 ................................................................................................. 10 BÖLÜM 2. LİTERATÜR TARAMASI..................................................................................... 12 BÖLÜM 3. MAKİNE ÖĞRENMESİ ALGORİTMALARININ VE PERFORMANS ÖLÇÜTLERİNİN İNCELENMESİ....................................................................... 15 BÖLÜM 4. UYGULAMA............................................................................................................ 22 4.1. Ön işleme .......................................................................................................... 23 4.2. Öznitelik seçimi................................................................................................ 27 4.3. Sınıflandırma .................................................................................................... 31 4.4. Araştırma bulguları........................................................................................... 32 4.6. İki Farklı Öznitelik Gruplarıyla Yapılan Deney Sonuçlarının Tartışılması ..... 41 iii BÖLÜM 5. SONUÇLAR ............................................................................................................. 43 KAYNAKLAR ......................................................................................................... 45 EKLER...................................................................................................................... 49MAKİNE ÖĞRENMESİ ALGORİTMALARININ HİBRİT YAKLAŞIMI İLE AĞ ANOMALİSİ TESPİTİ ÖZET İnternet, insanların iletişim kurması, bilgiye erişimi sağlaması, ticaret yapması ve birçok günlük aktivitesini gerçekleştirmesi için hayati bir öneme sahiptir. Ancak, bu artış beraberinde siber saldırılar ve tehditlerin de artmasına neden olmuştur. Siber saldırganlar her geçen gün daha sofistike yöntemler geliştirerek kişisel verileri çalmak, sistemlere zarar vermek veya hizmetleri engellemek gibi kötü niyetli eylemlerde bulunmaktadır. Bu durum, siber güvenlikte tespit sistemlerinin önemini daha da artırmıştır. Özellikle network anomali tespiti gibi sistemler, ağ trafiğindeki normal davranışları öğrenerek beklenmeyen veya anormal aktiviteleri tespit edebilmektedir. Bu sayede saldırıların erken aşamada tespit edilmesi ve önlenmesi sağlanmaktadır. Bu teknolojiler, bireylerin ve organizasyonların bilgilerini koruyarak dijital saldırıların potansiyel hasarını minimize etmeye yardımcı oluyor. Bu nedenle, siber güvenlik algılama sistemlerine yönelik araştırmalar kritik bir değere sahiptir. Hibrit modeller ile siber saldırı tespitinin yüksek başarıyla yapıldığı gözlemlenmiştir. Ağ anamolisinde kullanılan makine öğrenmesi algoritmalarının performansları genellikle KDD Cup 1999 veri kümesi üzerinde değerlendirilmiştir. Araştırmada, genellikle yüksek doğruluk seviyeleri gösterdikleri ve literatürde sıkça tercih edildikleri için Karar Ağacı (DT), Lojistik Regresyon (LR), Naive Bayes (NB), Rastgele Orman (RF) ve En Yakın Komşu (KNN) makine öğrenimi yöntemleri test edilmiştir. Veri madenciliği ve makine öğrenimi tekniklerinin ağ güvenliği alanındaki etkinliğini değerlendirmek amacıyla iki farklı hibrit öznitelik indirgeme yöntemi olan PCA + RFECV ve RFECV + FS yöntemleri karşılaştırılmıştır. PCA + RFECV yönteminde, temel bileşen analizi ile boyut indirgeme yapılmış ve ardından Recursive Feature Elimination with Cross-Validation (RFECV) yöntemi ile en iyi öznitelikler seçilmiştir. Değerlendirme metrikleri olarak, Çapraz Doğrulama ve ROC eğrileri tercih edilmiştir; bu metriklerin seçimi, algoritmaların performansının kapsamlı ve objektif bir şekilde analiz edilmesini sağlaması amacıyla yapılmıştır. Öznitelik indirgeme uygulanmadan, RF sınıflandırıcısı %98,15 ile en yüksek doğrulukta iken, KNN %96,31 doğruluk, %97,41 kesinlik, %95,24 duyarlılık ve %96,31 F1 skoru ile dikkat çekmiştir. PCA + RFECV uygulamasında, KNN'nin metrikleri benzer kalmış fakat NB sınıflandırıcısında %61,93 doğruluk ile büyük bir düşüş gözlemlenmiştir. RFECV + FS kullanıldığında, KNN %96,68 doğruluk, %97,76 kesinlik, %95,64 duyarlılık ve %96,69 F1 skoru ile öne çıkmıştır, bu da öznitelik indirgeme yöntemlerine duyarlılığını vurgulamaktadır. Sonuçlar, öznitelik seçiminin sınıflandırma performansındaki kritik rolünü vurgulamakta olup, veri kümesinin boyutunu azaltma, anlamlı öznitelikleri seçme ve hibrit yöntemler kullanma stratejilerinin sınıflandırma performansını artırabileceğini ortaya koymaktadır.NETWORK ANOMALY DETECTION WITH A HYBRID APPROACH OF MACHINE LEARNING ALGORTHMS ABSTRACT The Internet has become an essential medium for people to communicate, access information, conduct business, and carry out many daily activities. However, this surge has also led to a rise in cyberattacks and threats. Cyber adversaries are increasingly devising sophisticated methods to steal personal data, harm systems, or disrupt services with malicious intent. This escalation underscores the critical importance of cybersecurity detection systems. Particularly, network anomaly detection systems, which learn normal behavior patterns in network traffic, can identify unexpected or anomalous activities. This facilitates the early detection and mitigation of attacks. Strengthening and developing cybersecurity detection systems is of paramount importance in today's digital landscape. These systems safeguard individual users and institutions by protecting their data and information, thereby minimizing potential damages from cyberattacks. Moreover, they prevent service interruptions, ensuring the seamless operation of the Internet. In this context, research and studies on cybersecurity detection systems hold immense significance. The process of anomaly detection, aimed at identifying unexpected or deviant behaviors in datasets, is conducted through various techniques, prominently including machine learning, statistical methods, and data analysis techniques. This detection primarily focuses on identifying values that are either above or below the norm and holds critical importance across various domains. Anomaly detection techniques are primarily categorized into three main types: Point Anomaly, which defines situations where a single data point significantly deviates from the rest, such as an unexpected high transaction amount in a bank account. Contextual Anomaly pertains to the identification of a data point that is abnormal in relation to other data within a specific context; this could involve an unexpected change in network traffic. Collective Anomaly refers to the deviation of a combination of multiple features or attributes from the general behavior pattern; employee performance evaluations serve as an example for this kind of analysis. Applications of these detection methods span a wide range, from optimizing business processes to identifying potential threats. Network Anomaly Detection is utilized to identify unexpected behaviors in computer networks. It operates based on three main methods: Signature-Based, which detects pre-defined patterns; Behavior-Based, which distinguishes between normal and abnormal behaviors using statistical parameters; and Machine Learning-Based, which classifies new and unknown anomalies through trained models. These techniques are of critical importance for optimizing the security and performance of networks. x Network attacks refer to threats aimed at computer networks and connected devices. The objective of these attacks is to engage in malicious activities such as seizing network resources, stealing data, causing service disruptions, or crashing the system. For instance, DDoS attacks aim to disrupt the service by flooding the network with excessive traffic. UDP Flood attacks can exhaust target system resources by persistently sending a large amount of UDP traffic. Smurf attacks cause service interruptions by bombarding network devices with deceptive ICMP Echo Request messages. Teardrop attacks induce crashes in the target machine by using faulty fragment information. Botnet attacks orchestrate infected devices to create service disruptions. Clickjacking permits malicious actions without the user's knowledge, while DRDoS attacks amplify the attack impact using reflection techniques. On the other hand, malware attacks seize devices with malicious software, and Man-in-theMiddle attacks monitor and alter communication within the network. Ransomware attacks demand payment from users, while password cracking attacks aim to decode passwords. Social engineering attacks target the theft of personal information; whereas SQL injection, XSS, and phishing attacks target websites. ARP, DNS, and IP spoofing attacks misdirect network traffic with the intention of stealing or monitoring information. Ping of Death and SYN Flood attacks target disrupting network services. Popular test datasets used for network anomaly detection include NSL-KDD, UNSW-NB15, CICIDS2017, DARPA 1998, and KDDCUP99. NSL-KDD is an improved version of the KDD Cup 1999 dataset and contains 41 different network attack types. UNSW-NB15 consists of real network traffic data and is a detailed labeled set with 49 features; CICIDS2017 has 80 million event records encompassing 15 network attack types. DARPA1998 includes attacks conducted on a real network along with normal traffic, while KDDCUP99 is based on 5 million data samples obtained from a real computer network and has an imbalanced structure. These datasets serve as significant tools for network security and the training of machine learning algorithms. In this research endeavor, we employed the Anaconda distribution and crafted the code using Python in the Jupyter Notebook environment. Anaconda serves as both a package handler and an environment, encompassing Python and various tools tailored for data manipulation and machine learning. Key libraries like Sklearn, Numpy, and Pandas played a role in our study. The machine used for this research runs a 64-bit Microsoft Windows OS and boasts suitable technical features. • CPU: 11th Gen Intel(R) Core(TM) i5-1135G7 @ 2.40GHz 2.42 GHz • RAM: 16 GB In this study, an initial overview of network attacks was presented, followed by an extensive literature review. Conducted researches were thoroughly evaluated, and frequently used supervised machine learning algorithms and metaheuristic algorithms for network anomaly detection were identified. Studies where machine learning and metaheuristic algorithms were used in a hybrid manner were also analyzed. The performance of machine learning algorithms was tested on the KDD Cup 1999 dataset. During the data preprocessing phase, data related to attacks and xi normal traffic in the dataset were distributed evenly, with attack data labeled as 1 and normal traffic data labeled as 0. Missing values in the dataset were filled using the calculated median value. Categorical attributes (such as protocol_type, flag, service) were digitized using the one-hot encoding method. To scale the data and ensure they are on the same scale, the columns src_bytes and dst_bytes underwent normalization. Furthermore, a correlation matrix was calculated to measure the relationship between the features in the dataset, and highly correlated values were identified. In this case, the PCA method was employed to minimize the relationship between the features. In the study, classification was performed using machine learning algorithms such as Decision Tree (DT), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and K-Nearest Neighbors (KNN). Cross-Validation and ROC curves were employed as evaluation metrics. Additionally, to evaluate the role of data mining and machine learning methods in the field of network security, two distinct hybrid feature reduction methods, namely PCA + RFECV and RFECV + FS, were compared. In the PCA + RFECV method, dimensionality reduction was conducted using principal component analysis, followed by the selection of the best features through the Recursive Feature Elimination with Cross-Validation (RFECV) method. In this method, the Random Forest (RF) classifier was observed to achieve the highest accuracy results, while the K-Nearest Neighbors (KNN) classifier was successful in terms of the precision metric.On the other hand, with the RFECV + FS method, important features were first identified with RFECV, followed by the selection of the best features using the Forward Selection (FS) method. In this method, the KNN classifier stood out with the highest accuracy, precision, sensitivity, and F1 metrics. The results highlight the critical role of feature selection in classification performance, demonstrating that strategies of reducing dataset size, selecting meaningful features, and using hybrid methods can enhance classification performance. This study will be a valuable resource for academics researching network security and industrial organizations
The effect of aging process on bonding aluminum and steel in different MMA adhesive thicknesses
No full textİÇİNDEKİLER BEYAN........................................................................................................................ ii TEŞEKKÜR ................................................................................................................ i İÇİNDEKİLER .......................................................................................................... ii KISALTMALAR ....................................................................................................... v SİMGELER ............................................................................................................... vi TABLOLAR LİSTESİ............................................................................................. vii ŞEKİLLER LİSTESİ..............................................................................................viii ÖZET........................................................................................................................... x ABSTRACT............................................................................................................... xi BÖLÜM 1. GİRİŞ .......................................................................................................................... 1 BÖLÜM 2. RAYLI SİSTEMLER VE RAYLI SİSTEMLERDE YAPIŞTIRMA UYGULAMALARI.................................................................................................... 2 2.1. Raylı Sistemler.................................................................................................. 2 2.2. Raylı Sistemlerde Yapıştırma Uygulamaları..................................................... 3 BÖLÜM 3. YAPIŞMA VE YAPIŞTIRMA BAĞLANTILARI ................................................ 5 3.1. Yapışma............................................................................................................. 5 3.1.1. Yapışmaya etki eden kuvvetler.................................................................. 5 3.1.2. Yapıştırıcı ve yapıştırma ............................................................................ 7 3.2. Yapıştırıcıların Fonksiyonları ........................................................................... 7 3.3. Yapıştırma Bağlantılarının Avantaj ve Dezavantajları ..................................... 8 3.4. Yapıştırıcı Türleri.............................................................................................. 9 3.4.1. Kaynağına göre ........................................................................................ 10 3.4.1.1. Doğal yapıştırıcılar............................................................................ 10 3.4.1.2. Sentetik yapıştırıcılar ........................................................................ 11 3.4.2. Kimyasal kompozisyonuna göre.............................................................. 11 3.4.2.1. Termoset yapıştırıcılar ...................................................................... 11 3.4.2.2. Termoplastik yapıştırıcılar................................................................ 12 3.4.2.3. Elastomerik yapıştırıcılar .................................................................. 13 3.4.2.4. Yapıştırıcı alaşımlar ......................................................................... 14 iii 3.4.3. Fonksiyona göre...................................................................................... 14 3.4.3.1. Yapısal yapıştırıcılar ......................................................................... 14 3.4.3.2. Yapısal olmayan yapıştırıcılar........................................................... 15 3.4.4. Kürlenme mekanizmalarına göre ............................................................. 15 3.4.4.1. Kürlenmeyen yapıştırıcılar................................................................ 15 3.4.4.2. Fiziksel kürlenen yapıştırıclar........................................................... 16 3.4.4.3. Kimyasal kürlenen yapıştırıcılar ....................................................... 16 3.5. Yapışma Teorileri............................................................................................ 17 3.5.1. Mekanik tutunma teorisi .......................................................................... 18 3.5.2. Elektrostatik (Temas şarjı) teorisi........................................................... 19 3.5.3. Islatma teorisi........................................................................................... 20 3.5.4. Zayıf sınır tabaka teorisi .......................................................................... 21 3.5.5. Adsorpsiyon (Termodinamik) teorisi....................................................... 21 3.5.6. Difüzyon teorisi........................................................................................ 21 3.5.7. Kimyasal bağlanma teorisi....................................................................... 22 3.6. Yüzey İşlemleri ............................................................................................... 24 3.6.1. Yüzey hazırlığı......................................................................................... 25 3.6.2. Yüzey ön işlemi ....................................................................................... 25 3.6.2.1. Mekanik yüzey ön işlemleri.............................................................. 26 3.6.2.2. Fiziksel yüzey ön işlemleri................................................................ 26 3.6.2.3. Kimyasal yüzey ön işlemleri............................................................. 26 3.6.3. Yüzey işlem sonrası ................................................................................. 27 3.7. Yapıştırma Bağlantılarına Etki Eden Kuvvetler.............................................. 27 3.7.1. Basma yükü.............................................................................................. 28 3.7.2. Çekme yükü ............................................................................................. 28 3.7.3. Kesme yükü.............................................................................................. 29 3.7.4. Soyulma (Peel) yükü............................................................................... 29 3.7.5. Ayrılma (Cleavage) yükü......................................................................... 29 3.8. Yapıştırma Bağlantılarına Uygulanan Test Metotları ..................................... 29 3.8.1. Çekme testi............................................................................................... 31 3.8.2. Kayma kesme dayanımı testi ................................................................... 32 3.8.3. Soyma testi............................................................................................... 34 3.8.4. Kama testi ................................................................................................ 36 3.9. Yapıştırma Bağlantılarının Kırılma Mekanizması .......................................... 37 3.10. Yaşlanma....................................................................................................... 39 3.10.1. Sıcaklık................................................................................................... 41 3.10.2. Nem........................................................................................................ 42 3.10.3. Doğal ayrışma ........................................................................................ 43 3.10.4. İyonlaştırıcı radyasyon........................................................................... 44 3.10.5. Kimyasal bozunma................................................................................. 45 3.10.6. Çevresel stres kırılması (ESC)............................................................... 46 BÖLÜM 4. DENEYSEL ÇALIŞMALAR.................................................................................. 49 4.1. Deneylerde Kullanılan Malzemeler ................................................................ 49 4.2. Hazırlık İşlemleri............................................................................................. 50 4.3. Yapıştırma İşlemi ............................................................................................ 53 4.4. Deneyler ve Sonuçları ..................................................................................... 54 4.5. Deneysel Sonuçların Yorumlanması............................................................... 79 iv KAYNAKLAR ......................................................................................................... 82ALÜMİNYUM VE ÇELİĞİN FARKLI MMA YAPIŞTIRICI KALINLIKLARINDA YAPIŞTIRILMASINA YAŞLANDIRMA İŞLEMİNİN ETKİSİ ÖZET Bu tezin amacı, MMA yapıştırıcı ile birleştirilmiş alüminyum ile çeliğin yapıştırma bağlantısının farklı yapıştırıcı kalınlığı ve yaşlandırma faktörlerinin kayma mukavemetlerine etkisini incelemektir. Bu amaca yönelik olarak, kayma mukavemet testi için boyutları 100mm*25mm*2mm olan S235 ve AL 6005 malzemeler kullanılmıştır. Numuneler üzerinde 17mm*25mm boyutlarındaki alanlara MMA yapıştırıcı uygulanmıştır. Kalınlığın etkisinin incelenmesi için numunelere 0,1mm, 0,5mm, 1mm ve 2mm kalınlığında yapıştırıcılar uygulanmıştır. Her kalınlık için tuzlu su, UV etkisi, değişken sıcaklık ve nemin etkisi ile yaşlandırmaya maruz kalınmaması durumları için numuneler hazırlanmıştır. İlgili standart gereği mukavemet incelemesi için yapılan çekme testinde her bir grup için 5’er adet toplamda 80 adet yapıştırılmış bağlantı hazırlanmıştır. Yapıştırılan malzemeler kullanılan 3M DP 8405 MMA yapıştırıcının nihai mekanik özelliklere ulaşması ve doğru yapışma olması için çok önemli olan kürlenmesi için malzemeler 24 saat stantta bekletilmiştir. Kürlenen ürünlerden yaşlandırma etkilerine maruz kalacak olan çiftler ilgili test cihazlarına yerleştirilmiştir. Yaşlandırma işlemlerinin de bitmesi ile yapıştırılmış bağlantılara mukavemet tayini için çekme testi yapılmıştır. Test sonuçları, 1mm kalınlığa kadar mukavemetin düşmediğini fakat 2mm kalınlıkta dramatik bir şekilde düştüğünü göstermektedir. Kırılma modeli incelendiğinde 2mm yapıştırıcı kalınlığında çok düzgün bir kopma olmadığı gözlemlenmektedir. Bu durum, optimum yapıştırıcı kalınlığı seçiminin önemini ve kalınlığın belli bir seviyeden sonra artmasının negatif etkilerinin olduğunu göstermiştir. Sonuç olarak, farklı yapıştırıcı kalınlığı ve maruz kalınan ortam yapıştırma bağlantılarının kayma mukavemetini önemli ölçüde etkilemektedir.THE EFFECT OF AGING PROCESS ON BONDING ALUMINUM AND STEEL IN DIFFERENT MMA ADHESIVE THICKNESSES ABSTRACT The aim of this thesis is examine the effects of different adhesive thickness and ageing factors on lap shear strenght of adhesive bonded steel and aluminum joints with MMA adhesives. For this purpose, S235 and AL 6005 materials with measurements of 100mm*25mm*2mm were used for shear strength test. MMA adhesive was applied to 25 mm*25 mm sized areas on the samples. In order to examine the effect of thickness, 0.1mm, 0.5mm, 1mm and 2mm thick adhesives were applied to the samples. For each thickness, samples were prepared for the effect of salt water, UV effect, variable temperature and humidity and no ageing effect. In the tensile test performed for strength examination in accordance with the relevant standard, , 5 for each group and total of 80 bonded connections were prepared. The materials were kept in the stand for 24 hours in order to cure the 3M DP 8405 MMA adhesive, which is very important for the final mechanical properties and correct adhesion. The couples that will be exposed to the aging effects of the cured products are placed in the relevant test devices. After the aging process was completed, a tensile test was carried out to determine the strength of the bonded joints. Test results show that strength does not decrease up to 1mm thickness, but drops dramatically at 2mm thickness. When the fracture model is examined, it is observed that there is not a very smooth rupture at 2mm adhesive thickness. This showed the importance of optimum adhesive thickness selection and the negative effects of increasing the thickness after a certain level. As a result, different adhesive thickness and exposure environment significantly affect the shear strength of adhesive joint
The classification of wheat yellow rust disease based on a combination of textural and deep features
No full textYellow rust is a devastating disease that causes significant losses in wheat production worldwide and significantly affects wheat quality. It can be controlled by cultivating resistant cultivars, applying fungicides, and appropriate agricultural practices. The degree of precautions depends on the extent of the disease. Therefore, it is critical to detect the disease as early as possible. The disease causes deformations in the wheat leaf texture that reveals the severity of the disease. The gray-level co-occurrence matrix(GLCM) is a conventional texture feature descriptor extracted from gray-level images. However, numerous studies in the literature attempt to incorporate texture color with GLCM features to reveal hidden patterns that exist in color channels. On the other hand, recent advances in image analysis have led to the extraction of data-representative features so-called deep features. In particular, convolutional neural networks (CNNs) have the remarkable capability of recognizing patterns and show promising results for image classification when fed with image texture. Herein, the feasibility of using a combination of textural features and deep features to determine the severity of yellow rust disease in wheat was investigated. Textural features include both gray-level and color-level information. Also, pre-trained DenseNet was employed for deep features. The dataset, so-called Yellow-Rust-19, composed of wheat leaf images, was employed. Different classification models were developed using different color spaces such as RGB, HSV, and L*a*b, and two classification methods such as SVM and KNN. The combined model named CNN-CGLCM_HSV, where HSV and SVM were employed, with an accuracy of 92.4% outperformed the other models. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
On the earthquake-related damages of civil engineering structures within the areas impacted by Kahramanmaraş earthquakes
No full textTwo major earthquakes occurred on the Eastern Anatolian Fault Line (EAF) on February 6, 2023, with an interval of nine hours. These earthquakes, measuring Mw 7.7 and Mw 7.6, were centered in the districts of Pazarcık and Elbistan in the province of Kahramanmaraş. They directly affected 11 provinces (Kahramanmaraş, Hatay, Adıyaman, Osmaniye, Gaziantep, Şanlıurfa, Malatya, Diyarbakır, Adana, Kilis, and Elazığ) in the Eastern and Southeastern Anatolia, caused significant loss of life and property. This study aims to present the field investigation and performance evaluation of engineering structures in the mentioned cities. The types of damages occurring in the reinforced concrete (RC) and masonry buildings, historical and industrial structures, bridges, and mosques were given in detail. According to the data of the Ministry of Treasury and Finance of Türkiye, it has been reported that the cost of these earthquakes is approximately 103.6 billion dollars, which corresponds to nine percent of Türkiye's national income expectation for 2023 and causes damage and losses of approximately six times more than the 1999 Marmara earthquake. In the areas affected by earthquakes, many of the errors determined by professionals from previous earthquakes still exist today
Investigation of the effects of filler metal and number of passes used in AISI 304 stainless steel-copper joints by TIG welding
No full textIn this study, the effects of different filler materials, number of passes, and bevel preparation on the possible discontinuities, microstructure, and mechanical properties of the welded joint were investigated for the joining of Tungsten Inert Gas welding with commercially pure copper/AISI 304 stainless steel. Due to insufficient heat input, a lack of penetration discontinuity was observed in the weld, in which a single pass and 309L filler material was used. While discontinuities decreased in double-pass welding with 309L filler material, the tensile strength increased by approximately 18%. No discontinuities were observed in the double-pass weld with copper filler, and the tensile strength approached that of welds using stainless steel filler. A diffusion zone was formed at the fusion zone/base material cross-section in double-pass welded joints, increasing mechanical properties. The hardness distribution from stainless steel to copper base metal varies from 200 to 70 HV. In copper/stainless steel welding, the use of copper filler material and the choice of double-pass welding can be recommended for ideal properties. © 2023, International Institute of Welding
The Effect of Current Density on Friction and Wear Properties of Ni–W/PTFE Electro-Co-deposited Composite Coatings
No full textThe amount of heat transferred from the combustion chamber directly affects the combustion efficiency. Increasing the amount of heat transfer reduces the combustion efficiency. With thermal coating methods in engines, the amount of heat transfer is reduced and the combustion efficiency is increased. However, covering the entire combustion chamber with thermal material causes an increase in the knocking tendency and worsening of emission values in spark ignition engines. With the water injection method, emission values can be reduced significantly without worsening engine performance parameters. In the study, the upper surface of the piston was partially covered with MgOZrO2 ceramic material with high heat reserve in order to increase the engine efficiency. The coated engine was sprayed with water into the intake manifold at 10%, 20% and 30% by mass of instantaneous fuel consumption. By using the two methods at the same time, improvements in performance parameters and exhaust emissions have been achieved. In the TBL piston engine, power, torque, specific fuel consumption, effective efficiency and HC emissions improve, while NOx emissions increase. In the case of water injection into the intake manifold, the increased NOx emissions decrease without any deterioration in performance parameters. Improvements were found in engine torque and effective power by 4.1%, specific fuel consumption by 3.8% and effective efficiency by 3.9% at 20% water injection rate. Compared to standard engine data, a 20% reduction was achieved in NOx emissions at 30% water injection rate. In addition, reductions of up to 33% were detected in HC emissions at 20% water spraying rate. © 2023 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved
A new deep learning model combining CNN for engine fault diagnosis
No full textReal-time condition monitoring of electric motors and early diagnosis is of great importance for ensuring safe and reliable operation, preventing major accidents, and reducing production costs. Therefore, many intelligent fault diagnosis methods have been proposed. However, in industrial applications, the constantly changing loads of electric motors and the inevitable noise from the working environment cause a decrease in the performance of intelligent fault diagnosis methods. In this study, an effective and reliable deep learning model named the Combined One and Two-Dimensional Deep Convolutional Neural Network with Wide First-layer Kernels (WDD-CNN) is proposed for real-time condition monitoring and early fault diagnosis under noisy and changing operating conditions. The primary contribution of this study is the development of a fault diagnosis method that can operate in real-time to provide early detection of faults that may occur in electrical drive systems under operating conditions that are unpredictable and noisy. In addition, the proposed model works directly on raw signals, eliminating the complexity of preprocessing processes. The Case Western Reserve University (CWRU) dataset is used to test the performance and effectiveness of the proposed WDD-CNN model under different load conditions and for noise suppression. Additionally, the effectiveness of the model against data coming from a single sensor channel is also tested, and the results are recorded. The proposed method achieves 100% accuracy when tested with normal signals. Comparative results reveal that the WDD-CNN model outperforms other current state-of-the-art methods with an accuracy rate of 96.45% under different operating loads. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering
Investigating the Use of Methane as an Alternative Fuel in Diesel Engines: A Numerical Approach
No full textThe search for alternative fuels for diesel engines is being explored due to rising oil prices and increasing vehicle emissions. Due to its low cost and properties, natural gas is considered a suitable option for diesel engines. However, the costs required for research and misleading experimental setups can lead to time loss for researchers. Therefore, conducting computer simulations before experiments can reduce costs and provide faster access to desired data. Nonetheless, these simulations need to be compared with real experimental data. Hence, the study consists of two phases. In the first phase of the study, experiments were conducted with diesel fuel. Subsequently, a one-dimensional combustion model was developed in the AVL BOOST program. The established model was validated by comparing it with experimental data. Once the validated model was obtained, performance, emissions, and combustion analyses were carried out by adding different proportions of CH4(20%, 40%, 60%, and 80%) to diesel fuel using the model in AVL BOOST. As a result of the study, improvements in effective power and effective efficiency were achieved with the addition of varying proportions of CH4to the engine. Upon examining emitted exhaust emission values, it was observed that NOx emissions increased while CO emissions decreased. © 2024 Mashhad University of Medical Sciences. All rights reserved
Uzaktan Eğitimle Yürütülen Beden Eğitimi ve Spor Dersine İlişkin Öğretmen Görüşleri
No full textBu çalışmanın amacı COVID-ϭϵ pandemi sürecinde uzaktan eğitimle yürütülen beden eğitimi ve spor dersine yönelik beden eğitimi öğretmenlerinin görüşlerini incelemektir. Araştırmada nitel araştırma desenlerinden olgu bilim deseni kullanılmıştır. Araştırmanın çalışma grubunu, 2020-ϮϬϮϭ eğitim-öğretim yılı bahar döneminde Gaziantep ilinde görev yapan ϭϬ beden eğitimi ve spor öğretmeni oluşturmuştur. Araştırmada katılımcılar cinsiyet (kadın/erkek), okul kademesi (ortaokul-lise), okul deneyimi (0-ϭϬ yıl ve ϭϭ-ϮϬ yıl) dikkate alınarak maksimum çeşitlilik örneklemesi ile belirlenmiştir. Araştırma verileri görüşme yöntemiyle yarı yapılandırılmış soru formu kullanılarak elde edilmiştir. Elde edilen veriler içerik analizine tabi tutulmuş ve öğretme süreci (bilişsel öğrenme içerikleri ve hazırlıkları, psikomotor öğrenme içerikleri ve hazırlıkları), ölçme değerlendirme uygulamaları (öğrenme sürecini değerlendirme, öğrenme ürününü değerlendirme) yaşanılan sorunlar (sınırlı katılım, teknolojik yetersizlik, öğretmen yetersizliği, niteliksiz öğretim), ve niteliği artırmaya yönelik önlemler (motivasyonu artırmaya yönelik girişimler, teknolojik destek, diğer stratejiler) olmak üzere ϰ ana tema, bu ana temalara ait alt tema ve kodlara ulaşılmıştır. Sonuç olarak çalışmaya katılan öğretmenlerin beden eğitimi dersinin uzaktan yürütülmesiyle dersin amacına ulaşılmadığı görüşünde oldukları söylenebilir. Farklı öğretim yöntemleri ve modellerinin uzaktan eğitim sürecine nasıl entegre edilebileceği ile ilgili uygulamalı eğitimlerin verilmesi önemli görülmektedir