Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Karakose, Mehmet(7)
- Yilmazer, Merve(6)
- Boydak, Merve(4)
- Caliskan, Abuzer(4)
- Orhan, Ahmet(4)
- Aslan, Serpil(3)
- Buldu, Gul(3)
- Koseoglu, Murat(3)
- Uyanik, Hakan(3)
- Çil, Zeynel Abidin(3)
- Özceylan, Eren(3)
- Acikkapi, Mehmet Sahin(2)
- Benhala, B.(2)
- Gencer, Cetin(2)
- Gunay, Mihriban(2)
- Hasar, Ugur Cem(2)
- Kaya, Mehmet(2)
- Mete, Süleyman(2)
- Ozkaynak, Fatih(2)
- Qbadou, M.(2)
- Raihani, A.(2)
- Sengur, Abdulkadir(2)
- Ustundag, Aybike(2)
- Varol, A.(2)
- Varol, C.(2)
- Yildirim, Ozal(2)
- Ahmadi, Hadi Badri(1)
- Akkurt, Nevzat(1)
- Akpinar, Muhammed Halil(1)
- Ali, Leila Ait(1)
- Arslan, Samed(1)
- Aslan, Naim(1)
- Atar, Muhammed(1)
- Ay, Betul(1)
- Aydin, Galip(1)
- Aygunes, G.S.(1)
- Barroso, Joaquim J.(1)
- Barroso, Joaquim Jose(1)
- Baykara, Muhammet(1)
- Bürhan, Yücel(1)
Yayınlar
- BildiriYalnız künye2026LLM Based Software Test Scenario Generation System From Bug Descriptions
Gundem, Merve Yilmazer; Karakose, Mehmet
Large, user-interactive software systems require extensive software testing for safe deployment. Manually generated test cases by software testers require extensive time and effort, and their scope remains limited. In this study, a system based on LLM is proposed for automatic test cases and test steps generation from bug descriptions. In the proposed system, bug descriptions were first collected from the Defects4J Dataset. Keywords relevant to the bug scope were extracted from the descriptions. Structured automatic prompts were generated from the extracted keywords. The generated prompts were fed into the LLM as input, and software test cases appropriate for the bugs were generated. The method was evaluated on actual bug descriptions by comparing it to a general (baseline) prompt. Experimental results show that the lengths of the generated scenarios are similar, but the proposed method increases the keyword match rate with error descriptions to an average of 89 %. The findings reveal that keyword-driven prompts are effective in generating more reliable and error-specific test scenarios without requiring additional data.
- BildiriAçık Erişim2025LLM-Based Video Analytics Test Scenario Generation in Smart Cities
Yilmazer, Merve; Karakose, Mehmet
Rapid advances in the field of artificial intelligence have made significant contributions to the automation of software development and testing stages. Software created for use in various fields is tested with test scenarios created manually by software test experts or using test automation. Testing large-scale software with these methods complicates the testing phases because it requires increased human intervention and includes complex applications. In this study, an LLM-based scenario generation framework enhanced with prompt engineering is proposed for testing software to be used for video analysis in smart cities and smart campus areas. Thus, software test scenarios are created by strengthening large language models that are fast, flexible and have high learning ability using prompt engineering techniques. Test scenarios produced through LLM reinforced with prompt engineering techniques were evaluated with rarity and reality metrics and it was determined that more robust scenarios were produced compared to randomly generated test scenarios in the relevant field.
AtıfScopus3OpenAlex3
İncele - BildiriYalnız künye2025Scopus SJRQ4A Hybrid Machine Learning Architecture for Acoustic Fault Diagnosis in Electric Motors: Offline and Online Modes via Mobile Phone
Yordanov, Nikolay; Zhilevski, Marin; Mikhov, Mikho; Ertargin, Merve
This paper proposes a hybrid ML architecture for non-invasive acoustic fault diagnosis in electric motors and machines using a.NET MAUI cross-platform mobile app for short audio capture (5-20s), audio-to-image transformations and dual-mode inference: embedded ML.NET/ONNX models for offline preliminary detection and Azure ML-hosted deep CNN/RNN for online probabilistic multi-fault analysis. The primary objective of this paper is to demonstrate the benefits of the proposed architecture and to show the specific tools which can be used for implementation. Architectures (embedded, cloud, hybrid) are evaluated by listing main advantages and disadvantage, highlighting hybrid approach that combines offline resilience, reduced latency and keeping the possibility for complex analysis by using online hosted model. Sample mobile application design is added to showcase the proposed user experience.
- MakaleAçık Erişim2025Scopus SJRQ1Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals
Uyanik, Hakan; Kokum, Mehmet; Senturk, Erman; Freeshah, Mohamed; Ozcelik, Salih T. A.; Halil Akpinar, Muhammed; Celik, Serenay; Sengur, Abdulkadir
This study proposes a novel deep learning approach for predicting significant earthquakes (Mw ≥ 5.0 ) in Turkey using ionospheric Total Electron Content (TEC) data and space weather indices. ConvMixer is a lightweight CNN architecture that blends spatial and channel information using depthwise convolutions and pointwise layers. Inspired by vision transformers, it offers efficient image classification with fewer parameters and high performance. We developed a multi-input one-dimensional convolutional mixer (MI-1D-ConvMixer) model to classify TEC data from the preceding five consecutive days as either precursory to an earthquake on the 6th day or normal. The model incorporates six inputs: five 1D TEC signals and one 1D space-weather index array, including the global geomagnetic index (Kp), storm duration distribution (Dst), sunspot number (R), geomagnetic storm index (Ap-index), solar wind speed (Vsw), and solar activity index (F10.7) are also utilized to reveal non-seismic related pre-earthquake ionospheric variations. Our methodology involves two stages: 1) a preprocessing stage to enhance TEC signals and 2) an end-to-end training of the MI-1D-ConvMixer model. The model architecture features depth-wise and point-wise convolutions with patch embedding, utilizing uses four tunable hyperparameters: network depth, hidden dimension size, kernel size, and patch size. We used 196 earthquakes data from Turkey from 2010-2023, and TEC data from the TNPGN-Active GNSS stations. The dataset was split into 75% for training and 25% for testing. Performance metrics, including classification accuracy, sensitivity, specificity, and F1-score, are used for evaluation. Our model achieved a classification accuracy of 97.49%, demonstrating its potential for earthquake prediction systems. This research contributes to the field by introducing a novel deep learning architecture specifically designed for integrating TEC and space weather data for earthquake prediction. Future work should focus on validating the model’s performance in different geographical regions and investigating its limitations.
AtıfScopus7OpenAlex5
İncele - BildiriAçık Erişim2025Toplam Elektron Içeriǧi ve Uzay Iklim Verileri Kullanilarak Transfer Öǧrenme ile Deprem Öncüllerinin Analizi
Uyanik, Hakan; Şentürk, Erman; Köküm, Mehmet; Özçelik, Salih Taha Alperen; Akpinar, Muhammed Halil; Şengür, Abdulkadir
The stress changes occurring in the Earth's crust before earthquakes may cause some anomalies in the ionosphere and cause significant changes in the Total Electron Content (TEC) and Space Climate (SCC) parameters. The analysis of these changes offers a significant potential in the detection of earthquake precursors. In this study, the analysis of earthquake precursors was performed using TEC and UC data and the effectiveness of transfer learning and machine learning techniques was investigated. Features were extracted from five-day TEC and UC data using a pre-trained Multi-Input One-Dimensional Convolutional Mixer Network (ÇG-TB-EMA) and these features were classified with different machine learning algorithms. Within the scope of the study, earthquakes with a magnitude of Mw≥6.0 that occurred during the low and high solar activity periods of the 24th solar cycle were analyzed. The earthquakes were selected from the Far East region between 10°S-45°N latitudes and 120°E-160°E longitudes, where intense seismic activity is observed. Experimental studies have shown that during the low solar activity period, support vector machines (SVM) and artificial neural networks (ANN) models exhibited the highest performance with 96.04% accuracy and 96.08% F1 score, respectively. During the high solar activity period, the classification success decreased slightly, and SVM and ANN models achieved the best results with 90.28% accuracy and 89.86% F1 score. These findings reveal that the effect of solar activity on ionospheric variables should be taken into account in earthquake precursor detection. For future studies, long-term analyses are recommended to better understand the effects of solar activity.
- MakaleAçık Erişim2025Scopus SJRQ1Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches
Yilmaz, Mehmet; Yalcin, Erkut; Demir, Fatih; Ozdemir, Ahmet Munir; Atar, Muhammed; Gunes, Aysegul; Furtana Yalcin, Beyza; Cambay, Ertugrul
This study develops a deep learning-based automated system for detecting and segmenting earthquake-induced asphalt cracks, offering a rapid and reliable solution for post-disaster road condition assessments. Unlike traditional manual inspections, which are time-consuming and error-prone, our approach leverages advanced segmentation techniques to ensure accurate, pixel-level classification of various crack types. The main challenge of this study was determining the damage caused to highways by earthquakes with magnitudes greater than 7.0, which occur approximately once every 200 years. The most crucial step in the automatic detection of these damages is the reliable preparation of a high-accuracy dataset. To achieve this, pixel-based labels were created by experts in the construction field by analyzing each pixel value. Following two major earthquakes, a unique dataset for segmenting roadway deterioration was created through intensive and detailed studies. This study aims to present the performance results of popular deep learning-based segmentation models in an unbiased manner, providing a feasible infrastructure for future real-time applications. The innovative aspect of this research lies in the creation of a unique post-earthquake dataset, collected and labeled from highways affected by the February 6, 2023 earthquakes in Turkey (Mw = 7.7 and Mw = 7.6). Deep learning models, including SegNet, Attention SegNet, U-Net, FCN (8s), and DeepLab, were trained and tested on this dataset. Among these, the SegNet model achieved the best performance with an average accuracy of 86.72%, precision of 92.99%, and sensitivity of 78.45%. By demonstrating superior performance metrics compared to existing methods, this study provides a robust framework for future infrastructure monitoring and maintenance strategies, ensuring safer and more resilient transportation networks in disaster-prone regions.
AtıfScopus6OpenAlex4İncele - BildiriYalnız künye2024Exploring Triboelectric Properties in Sputtered Si(100)/SiO2/Cr/ZnO Thin Films - Ag/FEP Using Contact Speration Technique for Renewable Energy Harvesting
Yüzüak, Gizem Durak; Benhala, B.; Raihani, A.; Qbadou, M.
Currently, there is an ongoing inquiry into triboelectric nanogenerators (TENG), which use the triboelectric phenomenon to capture electrical energy from mechanical vibrations. The work aims to figure out the magnetron sputtered Cr/ZnO-based films on SiO2/Si(100) substrates with the construction of a Cr/ZnO-Ag/FEP tribo-based nanogenerator (TENG) and a specific emphasis on measuring TENG properties. The structural investigation of these films was conducted using X-ray diffraction (XRD) photoluminescence (PL), attenuated total reflectance fourier-transform infrared spectroscopy (AT-FTIR), and current - voltage (I-V) analysis in contact-separation mode (CSM). The Cr/ZnO-Ag/FEP TENG has a notable instantaneous maximum output voltage of around 63 V and a current density of 7 μA/cm2 when compared to other nanogenerators based on semiconductors, as determined by a comparative analysis. The results of this study provide useful information on the possible application of optimized magnetron sputtered Cr/ZnO-based films in triboelectric and energy harvesting systems, which might contribute to the progress of sustainable and renewable energy technologies.
- BildiriYalnız künye2024Performance Analysis of Attention Mechanisms for Melanoma Cancer Detection
Dogan, Gurkan
The deadliest type of skin cancer worldwide is melanoma. If melanoma skin cancer is detected and treated at an early stage, the mortality rate can be significantly reduced. In particular, convolutional neural networks, one of the deep learning techniques that have achieved remarkable success in recent years in areas such as computer vision, natural language processing, signal processing, etc., can detect this type of cancer automatically, quickly and with high accuracy. Moreover, the accuracy performance of the neural network can be further increased by using attention mechanisms together with convolutional neural networks. However, attention mechanisms have not been used correctly and effectively in the neural network models proposed so far for the detection of melanoma skin cancer. This study examines the effect of attention mechanisms on the accuracy performance of a convolutional neural network and guides researchers to use these mechanisms correctly and effectively. To achieve this, a base model based on a convolutional neural network was built. Then, seven different attention mechanisms were integrated separately into this basic model and the results of the experimental tests of each were presented.
AtıfScopus1OpenAlex1İncele - BildiriAçık Erişim2024A Deep Learning-Based Approach for Damage Detection in Cultural Heritage Images and Generating Heat Maps
Ogdu, Cagatay Umut; Yilmazer, Merve; Karakose, Mehmet
Cultural structures may be damaged and damaged over time due to external factors such as natural and social disasters, which may lead to the loss of national assets if cultural assets are not protected. Therefore, the protection of cultural heritage is of great importance not only for today's generations but also for future. In recent years, deep learning and image processing have made great progress and have become very effective for detecting and analyzing patterns. These techniques can also be used to solve complex problems such as damage detection of structures in the field of cultural heritage. Deep learning methods can be used to determine the current status of structures and detect possible damage by working on images obtained from different angles of structures, while also reducing human errors. In this study, as an important step for the protection of cultural heritage, a detection method was developed using deep learning models on damaged and intact cultural heritage building images. The data set used in the study consists of damaged and intact cultural heritage building images. An attempt was made to determine the model that gave the best results by using pre-trained models with the Transfer Learning method. Heat maps were created using the Grad-CAM method to identify damaged areas on the trained models, thus making it possible to more clearly identify the damaged areas in the image. This method can play an important role in preserving cultural heritage. It can be used to detect damaged areas and direct them to repair.
AtıfScopus3OpenAlex4İncele - BildiriAçık Erişim2024A New Approach to Detecting Free Parking Spaces Based on YOLOv6 and Keyframe Detection with Video Analytics
Yilmazer, Merve; Karakose, Mehmet; Tanberk, Senem; Arslan, Samed
With urbanization and population growth, automobile use has also increased. As a result of this, there is a need to expand parking management and inspection systems to ensure safe and efficient use of parking areas. A new deep learning-based method has been proposed as an alternative to traditional systems to control free spaces in parking areas monitored by CCTV security cameras. A large amount of video data needs to be processed for parking areas recorded with video cameras 24 hours a day, 365 days a year. Repeating frames in videos cause overlearning in deep learning models. It also increases memory and computation cost. For this reason, keyframe detection based on sampling was first performed on the sample video of the Dragon Lake Parking (DLP) dataset and a summary of the dataset was obtained. Then, empty parking areas were detected by YOLOv6 deep neural network. It has been observed that the model can detect empty parking areas at a rate of 96.6% mAP.
AtıfScopus7OpenAlex5İncele - BildiriYalnız künye2024An Analysis of Dependence Relationships Between Covid-19 Pandemic and Environmental Conditions Cases Study in Indonesia, England, and Türkiye
Handhayani, Teny; Caglar, Durdane Kocacoban; Wibowo, F.W.
This study explores the causal relationships between environmental conditions and Coronavirus disease 2019 (COVID-19) using Bayesian networks in three geographically distinct cities: Jakarta (Indonesia), London (England), and Istanbul (Türkiye). The datasets collected from these cities span from January 2020 to October 2022, encompassing a variety of environmental variables (Particulate Matter (PM2.5), temperature, humidity, precipitation, wind speed, and Ultraviolet (UV) index) and COVID-19 variables (new cases, deaths, and population density). By using the PC algorithm and Greedy Equivalence Search (GES) to generate causal graphs, and applying the Intersection-Validation (InterVal) method to derive agreement graphs, this research reveals significant geographical differences in how environmental factors influence COVID-19 dynamics. The Peter-Clark (PC) algorithm identifies conditional dependencies, while GES optimizes the structure of the causal graph. The study highlights the significant impact of temperature, UV index, and humidity on COVID-19 dynamics, emphasizing the importance of localized public health strategies tailored to specific environmental conditions. These findings contribute to the understanding of environmental influences on COVID-19 and offer valuable insights for future research and policy-making aimed at mitigating the impact of pandemics in diverse geographical settings.
AtıfScopus3OpenAlex2İncele - BildiriYalnız künye2024Assessing Environmental Pollution and Noise Levels at End-of-Life Vehicle Recycling Centers: A Comparative Study
Harun, Zambri; Akkurt, Nevzat; Hashim, Hashimah; Molla, Altaf Hossain; Cahyadi, Mokhamad Nur; Zuhal, Lavi R.
This study investigates the environmental impact of end-of-life vehicle (EL V) recycling operations by assessing pollution parameters and noise levels at formal and informal recycling centers compared to a green zone reference area. U sing an integrated monitoring system. This research measured concentrations of particulate matter (PM2.5, PMlO), nitrogen dioxide (N02), carbon dioxide (C02), ozone (03), and noise levels at selected sites in Selangor, Malaysia. Results reveal elevated pollutant concentrations and noise levels at both formal and informal EL V recycling centers relative to the green zone reference area. Diurnal variations in pollutant levels highlight the influence of operational activities and atmospheric conditions. The findings underscore the need for stringent regulatory measures and pollution control strategies to mitigate environmental and health risks associated with EL V recycling activities and promote sustainable practices in the recycling industry.
AtıfScopus2OpenAlex2İncele - MakaleAçık Erişim2023Scopus SJRQ1An Integrated Approach for Assessing Suppliers Considering Economic Sustainability Innovation
Ahmadi, Hadi Badri; Pamucar, Dragan; Pourhejazy, Pourya; Kaya, Sema Kayapinar; Liou, James J. H.
Innovation plays a key role for improving the sustainability performance of corporations. A truly sustainable innovative firm needs to take the social, environmental, and economic aspects of sustainable innovation into consideration. However, limited studies have investigated the economic aspect of sustainable innovation, especially in the context of supplier evaluation from emerging economies. To deal with this issue, this manuscript puts forward a criteria decision framework for evaluating economic sustainable innovative suppliers. A new methodology based on fuzzy Full Consistency Method (FFUCOM) and Improved Combinative Distance-based Assessment (ICODAS) is developed and extended using interval rough Dombi-Bonferroni operators. In the developed approach, the FFUCOM is employed to determine the criteria weights, and the ICODAS is responsible for assessing and ranking the suppliers. A case study from the manufacturing sector of an emerging economy is considered for validating the developed framework and decision model. The findings introduce the 'financial resumption of products' as the most critical economic innovation criterion for evaluating suppliers. This article significantly helps decision-makers and practitioners to better comprehend the underpinnings of sustainable innovation economic dimension, particularly when assessing their suppliers. Furthermore, the applicability and validity of the proposed model was confirmed by the results of sensitivity analysis. Findings demonstrated that the proposed method is a solid and useful MCDM framework, and its outputs are precise and acceptable.
AtıfScopus9OpenAlex6İncele - BildiriYalnız künye2023Investigation of corrosion behavior of ZrN-based PVD coatings on Ti-29Nb-13Ta-4.6Zr alloy in Hank's solution
Ali, Leila Ait; Aslan, Naim; Yilmazer, Hakan; Niinomi, Mitsuo; Sen, Anil; Dikici, Burak; Benhala, B.; Raihani, A.; Mansouri, K.; Qbadou, M.
In this study, the TiNbTaZr (TNTZ) alloys, which do not contain toxic and allergic elements, have been coated with hard ZrN physical vapor deposition (PVD) coatings to increase their in-vitro corrosion resistance. In-vitro electrochemical behavior of the ZrN coating and uncoated TNTZ alloy was evaluated using open circuit potential (OCP) and potentiodynamic polarization (PDS) techniques, respectively. The characterizations of the coatings were carried out using scanning electron microscopy (SEM), energy dispersion spectroscopy (EDS), and X-ray diffraction (XRD). The SEM examinations showed that ZrN coating is homogeneous. Besides, ZrN coating had a higher corrosion resistance than uncoated TNTZ alloy in Hank's solution.
- BildiriAçık Erişim2023Chat with ChatGPT: Access to cultural heritage with ChatGPT
Yilmazer, Merve; Karakose, Mehmet
Cultural heritage, in the most general sense, refers to the historical accumulations from the past to the present. Digital systems are being integrated as an alternative to physical interventions for the management, maintenance and supervision of cultural heritage sites. For these purposes, advanced intelligent systems such as machine learning and deep learning are generally used. In this study, the competence of ChatGPT, an artificial intelligence robot developed by OpenAI with a high learning rate, in the field of cultural heritage is investigated. Questions such as cultural heritage, digital cultural heritage, protection of cultural heritage, etc. were asked to ChatGPT, and the timeliness and adequacy of the answers produced in response to the questions were evaluated. In addition, a comparison of experimental studies in the literature for the preservation and cultural heritage digitalization sites is also included in this study.
AtıfScopus6OpenAlex4İncele - BildiriAçık Erişim2023Yolov5 Based Fault Detection Approach in Railway Components
Yilmazer, Merve; Karakose, Mehmet
For safe transportation on the railway, the faults occurring in its components must be detected and repaired. Recently, fault detection methods have been developed on railway visual data using methods such as image processing, machine learning and deep learning. Especially since deep learning-based algorithms have a self-learning structure, the data used in model training and testing significantly affect the performance of the model. In the proposed method within the scope of this study, railway visual data were obtained using autonomous drone. YOLOv5 deep neural network is trained to detect faults that may occur in various components such as rail, fastener, sleeper and ballast in the railway. In order to increase the efficiency of the model, images obtained under different lighting conditions were included in the data set. In addition, by using the OpenCV library, the video stabilization process and the vibrations that occur during data acquisition in the video are eliminated. Thus, with the proposed method, it has been shown that the errors due to many components in the railway can be detected with a mAP rate of 94.5%.
AtıfScopus5OpenAlex3İncele - BildiriYalnız künye2022Privacy Concerns and Measures in Metaverse: A Review
Canbay, Yavuz; Utku, Anil; Canbay, Pelin; Ozbudak, F.; Sagiroglu, S.; Selcuk, A.A.
Metaverse is the new buzzword in today's digital world. It is a new internet application and a new form of the social world that integrates new technologies such as; Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Blockchain (BC),etc. The metaverse can be seen as a virtual world that mimics the real world. Most things that can be done in the real world, such as shopping, making new friends, attending concerts, playing games, and so on, can be done differently in Metaverse. While a person who represents himself with his presence in the real world realizes this representation in the digital world with his personal data. Hence, the Metaverse exploits personal data to create and maintain this virtual world. At this point, privacy concerns of users in Metaverse occur, and Metaverse Service Providers (MSP) should take these concerns into account. This paper focuses on privacy concerns in Metaverse, presents some measures in order to minimize these concerns, and provides a comprehensive list of personal data collected and processed in Metaverse.
AtıfScopus44OpenAlex53İncele - MakaleAçık Erişim2022Scopus SJRQ1Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network
Imak, Andac; Celebi, Adalet; Siddique, Kamran; Turkoglu, Muammer; Sengur, Abdulkadir; Salam, Iftekhar
Panoramic and periapical radiograph tools help dentists in diagnosing the most common dental diseases, such as dental caries. Generally, dental caries is manually diagnosed by dentists based on panoramic and periapical images. For several reasons, such as carelessness caused by heavy workload and inexperience, manual diagnosis may cause unnoticeable dental caries. Thus, computer-based intelligent vision systems supported by machine learning and image processing techniques are needed to prevent these negativities. This study proposed a novel approach for the automatic diagnosis of dental caries based on periapical images. The proposed procedure used a multi-input deep convolutional neural network ensemble (MI-DCNNE) model. Specifically, a score-based ensemble scheme was employed to increase the achievement of the proposed MI-DCNNE method. The inputs to the proposed approach were both raw periapical images and an enhanced form of it. The score fusion was carried out in the Softmax layer of the proposed multi-input CNN architecture. In the experimental works, a periapical image dataset (340 images) covering both caries and non-caries images were used for the performance evaluation of the proposed method. According to the results, it was seen that the proposed model is quite successful in the diagnosis of dental caries. The reported accuracy score is 99.13%. This result shows that the proposed MI-DCNNE model can effectively contribute to the classification of dental caries.
AtıfScopus107İncele - BildiriAçık Erişim2022Railway Condition Monitoring and Fault Detection Based on YOLOv4
Yilmazer, Merve; Karakose, Mehmet
Regular maintenance of the line is critical for safety in railway transportation, which constitutes an important part of the transportation system. Due to the high error rate of manual fault detection methods, non-contact fault detection methods have been developed. Railway switch state and level crossing faults are frequently encountered in the occurrence of train accidents. A new method based on YOLOv4 has been proposed for condition monitoring and fault detection in these rail sections. The YOLOv4 deep neural network was trained using four class label datasets consisting of real railway visual data. Model evaluated using test data. Performance evaluation was made using evaluation metrics. Experimental results showed that the model could detect correctly with 96.8% accuracy.
AtıfScopus8OpenAlex9İncele - BildiriAçık Erişim2021Control of automatic fire extinguishing operation for 5 forest areas with SCADA
Ustundag, Aybike; Gencer, Cetin
The number of forest fires has increased significantly in recent years. Interventions to forest fires from air and land are very difficult and dangerous. In addition, since these interventions are time-consuming, forest fire can spread to a much wider area. In this study, an automatic fire extinguishing system was designed using SCADA for 5 forest regions up to 5 hectares. In the event of a fire in the system, the smoke sensor in the fire area becomes active and the IP camera image in this area is displayed on the screen. The pressurized water flushing tap, which automatically rotates 360 degrees, works in the area. If desired, automatic operation can be canceled and manually adjusted water flushing tap can be operated. The system gives a warning when the tank water drops below 1 ton. The aim of this study is to automatically intervene early in any fire that may occur in the forest and to control the fire before it spreads.
AtıfScopus1OpenAlex1İncele