Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this communityhttps://hdl.handle.net/11413/11
Browse
Browsing Mühendislik Fakültesi / Faculty of Engineering by Type "Article Early Access"
Now showing 1 - 12 of 12
- Results Per Page
- Sort Options
Publication Boosting the Visibility of Services in Microservice Architecture(Springer, 2023) TOKMAK, AHMET VEDAT; AKBULUT, AKHAN; Çatal, ÇağatayMonolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on. Service Oriented Architecture (SOA) surpassed monolithic architecture due to its reusability, platform independency, ease of maintenance, and scalability. Recent SOA implementations made use of cloud-native architectural approaches such as microservice architecture, which has resulted in a new challenge: the discovery difficulties of services. One way to dynamically discover and route traffic to service instances is to use a service discovery tool to locate the Internet Protocol (IP) address and port number of a microservice. In the event that replicated microservice instances are found to provide the same function, it is crucial to select the right microservice that provides the best overall experience for the end-user. Parameters including success rate, efficiency, delay time, and response time play a vital role in establishing a microservice's Quality of Service (QoS). These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality.Publication Characterization of Site Amplification by a Parametric Study(Taylor & Francis Inc., 2023) FERCAN, NAZİFE ÖZGE; Şafak, Erdal; Ansal, AtillaThe reliability of Vs30 and the performance of alternative time averaged shear wave velocities (Vsz) and shear wave travel times (Ttz) at various depths, z, were investigated for the estimation of site amplification and fundamental frequency (f0) characterization by considering the linear and nonlinear soil behavior. The study revealed that alternative parameters performed better than Vs30 and the best performing z parameters changed by switching from convex to concave theoretical profiles and by increasing ground motions. For a practical usage in site investigations, guidelines to estimate nonlinear soil amplification factor and fundamental frequency from the linear ones were presented.Publication Deep Learning-Based User Experience Evaluation in Distance Learning(Springer, 2023) SADIGOV, RAHIM; YILDIRIM, ELİF; KOCAÇINAR, BÜŞRA; AKBULUT, FATMA PATLAR; Çatal, ÇağatayThe Covid-19 pandemic caused uncertainties in many different organizations, institutions gained experience in remote working and showed that high-quality distance education is a crucial component in higher education. The main concern in higher education is the impact of distance education on the quality of learning during such a pandemic. Although this type of education may be considered effective and beneficial at first glance, its effectiveness highly depends on a variety of factors such as the availability of online resources and individuals' financial situations. In this study, the effectiveness of e-learning during the Covid-19 pandemic is evaluated using posted tweets, sentiment analysis, and topic modeling techniques. More than 160,000 tweets, addressing conditions related to the major change in the education system, were gathered from Twitter social network and deep learning-based sentiment analysis models and topic models based on latent dirichlet allocation (LDA) algorithm were developed and analyzed. Long short term memory-based sentiment analysis model using word2vec embedding was used to evaluate the opinions of Twitter users during distance education and also, a topic model using the LDA algorithm was built to identify the discussed topics in Twitter. The conducted experiments demonstrate the proposed model achieved an overall accuracy of 76%. Our findings also reveal that the Covid-19 pandemic has negative effects on individuals 54.5% of tweets were associated with negative emotions whereas this was relatively low on emotion reports in the YouGov survey and gender-rescaled emotion scores on Twitter. In parallel, we discuss the impact of the pandemic on education and how users' emotions altered due to the catastrophic changes allied to the education system based on the proposed machine learning-based models.Publication Design and Implementation of a Deep Learning-Empowered m-Health Application(Springer, 2023) AKBULUT, AKHAN; DESOUKI, SARA; ABDELKHALIQ, SARA; KHANTOMANI, LAYAL; Çatal, ÇağatayMany people are unaware of the severity of melanoma disease even though such a disease can be fatal if not treated early. This research aims to facilitate the diagnosis of melanoma disease in people using a mobile health application because some people do not prefer to visit a dermatologist due to several concerns such as feeling uncomfortable by exposing their bodies. As such, a skincare application was developed so that a user can easily analyze a mole at any part of the body and get the diagnosis results quickly. In the first phase, the corresponding image is extracted and sent to a web service. Later, the web service classifies using the pre-trained model built based on a deep learning algorithm. The final phase displays the confidence rates on the mobile application. The proposed model utilizes the Convolutional Neural Network and provides 84% accuracy and 72% precision. The results demonstrate that the proposed model and the corresponding mobile application provide remarkable results for addressing the specified health problem.Publication Multi-Modal Fusion Learning Through Biosignal, Audio, and Visual Content for Detection of Mental Stress(Springer London Ltd., 2023) GÜLİN, DOĞAN; AKBULUT, FATMA PATLARMental stress is a significant risk factor for several maladies and can negatively impact a person's quality of life, including their work and personal relationships. Traditional methods of detecting mental stress through interviews and questionnaires may not capture individuals' instantaneous emotional responses. In this study, the method of experience sampling was used to analyze the participants' immediate affective responses, which provides a more comprehensive and dynamic understanding of the participants' experiences. WorkStress3D dataset was compiled using information gathered from 20 participants for three distinct modalities. During an average of one week, 175 h of data containing physiological signals such as BVP, EDA, and body temperature, as well as facial expressions and auditory data, were collected from a single subject. We present a novel fusion model that uses double-early fusion approaches to combine data from multiple modalities. The model's F1 score of 0.94 with a loss of 0.18 is very encouraging, showing that it can accurately identify and classify varying degrees of stress. Furthermore, we investigate the utilization of transfer learning techniques to improve the efficacy of our stress detection system. Despite our efforts, we were unable to attain better results than the fusion model. Transfer learning resulted in an accuracy of 0.93 and a loss of 0.17, illustrating the difficulty of adapting pre-trained models to the task of stress analysis. The results we obtained emphasize the significance of multi-modal fusion in stress detection and the importance of selecting the most suitable model architecture for the given task. The proposed fusion model demonstrates its potential for achieving an accurate and robust classification of stress. This research contributes to the field of stress analysis and contributes to the development of effective models for stress detection.Publication PyTHang: an Open-s-Source Wearable Sensor System for Real-Time Monitoring of Head-Torso Angle for Ambulatory Application(Taylor & Francis Ltd., 2021) GÜRKAN, GÜRAYThis article presents the realization of a low-cost wearable sensor system and its Python-based software that can measure and record relative head-torso angle, especially in sagittal plane. The system is mainly developed to track head-torso angle during walk in a clinical study. The open-hardware part of the system is composed of a pair of triaxial digital accelerometers, a microprocessor, a Bluetooth module and a rechargeable battery unit. The reception of the transmitted acceleration data, visualization, interactive sensor alignment, angle estimation and data-logging are realized by the developed open-source graphical user interface. The system is tested on a tripod for verification and on a subject for practical demonstration. Developed system can be constructed and used for ambulatory monitoring and analysis of relative head-torso angle. Open-source user interface can be downloaded and developed for further (different) algorithms and device hardware.Publication Quality Factor Based Transducer Power Gain Expression(Emerald Group Publishing Ltd., 2023) ŞENGÜL, METİNPurposeIn the literature, while designing broadband matching networks, transducer power gain (TPG) is used to measure the transferred power. Generally, in TPG expressions, load and back-end impedances of the matching network are used. This study aims to derive a new quality factor-based TPG expression. Design/methodology/approachIn deriving the new expression, narrowband L type-matching network design approach is used and the new expression in terms of back-end quality factor, load quality factor and output port quality factor is obtained. Then, a broadband-matching network design approach using the derived TPG expression is proposed. FindingsTwo broadband double-matching networks are designed by using the proposed design approach using the derived TPG expression. Performances of the designed-matching networks are compared with the performances of the matching networks designed by means of simplified real frequency technique which is a well-known technique in the literature, and it is shown that they are nearly the same. Originality/valueIn broadband-matching problems, generally an impedance-based TPG expression is used, and it must be satisfied by the designed broadband-matching networks. But, in the literature, there is no quality factor-based TPG expression that can be used in broadband-matching problems. So, this gap in the literature has been filled by this paper.Publication Seismic Retrofit of Substandard RC Columns Using Sprayed Glass Fiber-Reinforced Mortar and Basalt Textile Reinforcement(Taylor & Francis Ltd., 2023) Ateş, Ali Osman; Hajihosseinlou, Saeid; Nasrinpour, Amin; Demir, Cem; CÖMERT, MUSTAFA; Maraşlı, Muhammed; İlki, AlperThis study investigates the seismic retrofit of substandard reinforced concrete (RC) columns through external jacketing of potential plastic hinge zones using sprayed glass fiber-reinforced mortar and basalt textile reinforcement. An innovative spraying method was used to apply the matrix material to the concrete surface. A total number of eight full-scale columns (four reference, four retrofitted) were tested under constant high axial load to capacity ratio and reversed cyclic lateral loading. Columns were constructed using low-strength concrete and transverse reinforcement with various spacing and inadequate hook detailing to mimic the columns in substandard structures. Test results are evaluated in terms of lateral load-drift ratio relationships, displacement ductility, stiffness degradation, energy dissipation, and residual displacements. Evaluation of the test results showed that the proposed technique is effective for seismic retrofit of substandard RC columns, particularly in terms of enhancement of displacement ductility, energy dissipation capacities, and reduction of residual displacements.Publication Seismic Retrofitting Of the 19(TH) Century Hirka-i Serif Mosque Using Textile Reinforced Mortar(Taylor & Francis Inc., 2022) Demir, C.; CÖMERT, MUSTAFA; KOÇAK, PINAR İNCİ; Dusak, S.; İlki, A.In this study, a novel retrofitting intervention on the Hirka-i Serif Mosque (constructed in 1851), which has a significant importance for Muslims due to its historical relic preservation unit keeping the cloak of the Prophet Muhammad, was illustrated. For this purpose, first the seismic performance of the mosque was investigated through site investigations and structural analyses. A 3D finite element model of the Mosque was established, and the structural system was analyzed under the combined effects of the vertical loads and seismic actions. Additionally, the existing damages observed on the walls and vaults were investigated thoroughly. By taking the existing damages and the analyses results into consideration, a rehabilitation and seismic retrofit scheme was proposed and applied by making use of innovative materials. Throughout the study, recommendations of the guideline for earthquake risk management of historical structures in Turkey (2017) has been considered.Publication Stacking-Based Ensemble Learning for Remaining Useful Life Estimation(Springer, 2023) Türe, Begüm Ay; AKBULUT, AKHAN; Zaim, Abdul Halim; Çatal, ÇağatayExcessive and untimely maintenance prompts economic losses and unnecessary workload. Therefore, predictive maintenance models are developed to estimate the right time for maintenance. In this study, predictive models that estimate the remaining useful life of turbofan engines have been developed using deep learning algorithms on NASA's turbofan engine degradation simulation dataset. Before equipment failure, the proposed model presents an estimated timeline for maintenance. The experimental studies demonstrated that the stacking ensemble learning and the convolutional neural network (CNN) methods are superior to the other investigated methods. While the convolution neural network (CNN) method was superior to the other investigated methods with an accuracy of 93.93%, the stacking ensemble learning method provided the best result with an accuracy of 95.72%.Publication Utilizing Metaheuristics to Estimate Wind Energy Integration in Smart Grids With A Comparative Analysis of Ten Distributions(Taylor & Francis Inc., 2024) Wadi, Mohammed; ELMASRY, WİSAM; Çolak, İlhami; Jouda, Muhammed; Küçük, İsmailRenewable energy presents the most favorable approach to address the escalating challenge of greenhouse gas emissions while simultaneously guaranteeing the safeguarding of the environment. This article utilizes ten different distributions to approximate the wind energy integration in smart grids. The employed distributions are Rayleigh, Poisson, Weibull, Normal, Gamma, Laplace, LogNormal, Nakagami, Birnbaum Saunders, and Burr. The parameters of each distribution are calculated based on metaheuristic methods such as particle swarm optimization and genetic algorithms. Six error criteria have been employed to evaluate the precision of introduced distributions and metaheuristic methods. The approximation is performed by utilizing the wind data collected over three years hourly in the Marmara region of Turkiye. The empirical findings indicate that Gamma, Burr, and Weibull distributions exhibit more significant superiority than the remaining distributions across all datasets.Publication VDIBA-Based Current-Mode PID Controller Design(World Scientific Publishing Co Pte Ltd., 2023) ORUÇOĞLU, UMUT CEM; Özer, Emre; Kaçar, FıratThis paper aims to bring a voltage differencing inverting buffered amplifier (VDIBA)based current-mode (CM) proportional integral derivative (PID) controller circuit. This CM PID controller is designed with a single VDIBA, three resistors, and two grounded capacitors. The proposed circuit is easy to design, and the control parameters can be tuned without changing the design configuration. A sensitivity analysis of the control parameters to electronic components has been conducted. The Simulation Program with Integrated Circuit Emphasis (SPICE) simulation has been performed using Taiwan Semiconductor Manufacturing Company (TSMC) 0.18 mu m complementary metal-oxide semiconductor (CMOS) technology parameters. An application circuit example is given to demonstrate the reliability of the proposed PID design. A comparison table of the PID controllers previously reported in the literature is also presented.