Mühendislik Fakültesi / Faculty of EngineeringMühendislik Fakültesi'ne ait yayınları kapsar.https://hdl.handle.net/11352/422024-03-28T23:11:52Z2024-03-28T23:11:52ZA Bi-Objective Traffic Signal Optimization Model for Mixed Traffic Concerning Pedestrian DelaysAkyol, GörkemSilgu, Mehmet AliÇelikoğlu, Hilmi BerkGöncü, Sadullahhttps://hdl.handle.net/11352/48792024-03-22T09:58:02Z2024-01-01T00:00:00ZA Bi-Objective Traffic Signal Optimization Model for Mixed Traffic Concerning Pedestrian Delays
Akyol, Görkem; Silgu, Mehmet Ali; Çelikoğlu, Hilmi Berk; Göncü, Sadullah
Urban traffic networks suffer in numerous ways from traffic congestion. Some of these adverse effects are increased travel times
of cars, buses, bicycle users, pedestrians etc., with the addition of excessive greenhouse gas emissions. Transportation engineers
and policy makers try to improve the quality of urban transportation systems by developing projects to enhance the pedestrian
experience, reduce private car usage, reduce total time spent in the network through different control strategies, and diminish the
detrimental effects. In this context, this study takes Connected and Automated Vehicles (CAVs) and pedestrians into account at
signal-controlled intersections. A novel intersection signal control optimization methodology that incorporates pedestrian delays
and vehicular emissions from CAVs is presented. Non-dominated sorting genetic algorithm-II is utilized to solve the multiobjective
optimization problem. For the emission calculations, the MOVES3 emission model is utilized. The proposed framework is tested
on real-world case study. Simulation experiments showed major improvements in pedestrian delays and lower emissions.
2024-01-01T00:00:00ZInvestigation of Industrial Structure Performances in The Hatay and Gaziantep Provinces During the Türkiye Eearthquakes on February 6, 2023Öztürk, SezerAltunsu, ElifGüneş, OnurSarı, Alihttps://hdl.handle.net/11352/48082024-03-15T06:21:31Z2024-01-01T00:00:00ZInvestigation of Industrial Structure Performances in The Hatay and Gaziantep Provinces During the Türkiye Eearthquakes on February 6, 2023
Öztürk, Sezer; Altunsu, Elif; Güneş, Onur; Sarı, Ali
The seismic events of magnitudes 7.7 and 7.6 that occurred in the Pazarcik and Elbistan districts of Kahramanmaras,
respectively, resulted in a disastrous aftermath characterized by a substantial loss of life, injuries
affecting a multitude of individuals, and extensive structural failures. The present investigation encompasses the
comprehensive assessments carried out by research team pertaining to industrial structures in the wake of these
catastrophic seismic events. These evaluations were centered on various categories of structures, including liquid
storage tanks, silos designed for the storage of grain-like materials, prefabricated reinforced concrete structures,
and low-rise steel industrial buildings. Wall buckling damage caused by the impulsive and convective effects of
the fluid has been detected in liquid storage tanks. Additionally, one of the tanks at the oil storage facility has
toppled on account of inadequate anchoring. Maize silos, displaying variations in design such as columnsupported
and ground-supported configurations, displayed noticeable structural damage at their base connections.
Additionally, the internal pressure generated on the silo walls during the seismic event led to structural
rupture, causing the discharge of grain-like agricultural commodities. Various types of damage were also
determined in precast structures. Inadequate connections between columns and beams, plastic hinges in columns,
and collapse of infill walls are examples of such damage. The significance of connections in low-rise steel
structures was also demonstrated in this earthquake. Furthermore, one contributing factor to structural damage
is the significant discrepancy in stiffness between different orientations of such structures. The study clarifies
both the manifestations of structural damages and their underlying causative factors related to strong ground
motion, while also delineating recommended precautionary measures.
2024-01-01T00:00:00ZLoad Estimation of Different Types of Domestic Users Using Machine Learning Methods and Optimal Battery SizingÜnal, Ümit Canİrek, HakanSancar, SemanurErenoğlu, Ayşe Kübrahttps://hdl.handle.net/11352/47502024-03-08T12:24:12Z2023-01-01T00:00:00ZLoad Estimation of Different Types of Domestic Users Using Machine Learning Methods and Optimal Battery Sizing
Ünal, Ümit Can; İrek, Hakan; Sancar, Semanur; Erenoğlu, Ayşe Kübra
gained ever-increasing importance as the world population grows, set to reach 10 billion by 2050. The urgency for sustainable and nature-friendly energy production, as well as efficient consumption, parallels the rising demand. Rapid urbanization and industrialization are increasing energy needs and greenhouse gas emissions, prompting countries to reduce emissions through policies and environmental protocols. This study explores the challenges of integrating variable, and weather-dependent renewable energy sources into the grid, which necessitates accurate energy consumption prediction. The load consumption of various domestic users is aimed to be predicted and battery sizing is intended to be optimized accordingly. Data from 15 households of varying sizes with 1 minute resolution, spanning over a year with minute-resolution, was used. Machine learning models, including LSTM, Random Forest Regressor, XGBoost, and Linear Regression were employed, with temperature, holidays, and sunrise/sunset times identified as significant features. The study extends beyond load prediction, promoting consumer savings through variable electricity prices and advocating for battery use for reliable electricity supply. This work represents a pioneering effort in battery optimization based on load prediction data, facilitating a balanced, economical, and sustainable power system.
2023-01-01T00:00:00ZDetecting SQL Injection Attacks by Binary Gray Wolf Optimizer and Machine Learning AlgorithmsArasteh, BahmanAghaei, BabakFarzad, BehnoudArasteh, KeyvanKiani, FarzadTorkamanian-Afshar, Mahsahttps://hdl.handle.net/11352/47492024-03-08T12:05:17Z2024-01-01T00:00:00ZDetecting SQL Injection Attacks by Binary Gray Wolf Optimizer and Machine Learning Algorithms
Arasteh, Bahman; Aghaei, Babak; Farzad, Behnoud; Arasteh, Keyvan; Kiani, Farzad; Torkamanian-Afshar, Mahsa
SQL injection is one of the important security issues in web applications because it allows an attacker to interact with the
application’s database. SQL injection attacks can be detected using machine learning algorithms. The effective features
should be employed in the training stage to develop an optimal classifier with optimal accuracy. Identifying the most
effective features is an NP-complete combinatorial optimization problem. Feature selection is the process of selecting the
training dataset’s smallest and most effective features. The main objective of this study is to enhance the accuracy,
precision, and sensitivity of the SQLi detection method. In this study, an effective method to detect SQL injection attacks
has been proposed. In the first stage, a specific training dataset consisting of 13 features was prepared. In the second stage,
two different binary versions of the Gray-Wolf algorithm were developed to select the most effective features of the
dataset. The created optimal datasets were used by different machine learning algorithms. Creating a new SQLi training
dataset with 13 numeric features, developing two different binary versions of the gray wolf optimizer to optimally select
the features of the dataset, and creating an effective and efficient classifier to detect SQLi attacks are the main contributions
of this study. The results of the conducted tests indicate that the proposed SQL injection detector obtain 99.68% accuracy,
99.40% precision, and 98.72% sensitivity. The proposed method increases the efficiency of attack detection methods by
selecting 20% of the most effective features.
2024-01-01T00:00:00Z