Graduate School of Engineering and Science

Permanent URI for this communityhttps://hdl.handle.net/10679/8952

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Now showing 1 - 20 of 37

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Airflow characteristics and thermal comfort of air diffusers
(ASME, 2023) Eraslan, Tolga Arda; Keskin, Cem; Mengüç, Mustafa Pınar; Mechanical Engineering; MENGÜÇ, Mustafa Pınar; Eraslan, Tolga Arda; Keskin, Cem
Indoor environment quality control is very important for building operations as occupant of buildings spend up to 90% of their time indoors. After Covid-19 pandemic break out, indoor environment quality has become even more crucial to the society because of health concerns. Indoor Environmental Quality (IEQ) covers conditions such as air quality, lighting, thermal conditions, ergonomics inside a building and their effects on the occupant or occupants of the building. Thermal conditions and air quality are usually achieved with the mechanical or natural ventilation systems or by HVAC equipment. As buildings became more complex structures, different airflow distribution systems to be developed to fulfill such requirements. For this purpose, not only the airflow distribution systems need to be modified but also diffusers that provide which supplies/draws air to/from systems are to be improved. Detailed analysis of these subcomponents is needed to assure that such devices provide high levels of comfort effectiveness and energy efficiency. The objective of this study is to develop a comprehensive analysis for air characterization and indoor air regime of different diffusers (square diffuser, operable flap diffuser) and their effect on comfort level of occupants Fanger’s model of thermal comfort is used with CFD simulations and a tool is considered for the validation experiments. Using more than 16 thermal sensors including one on a mannequin head, on a table and at foot level, the readings were correlated by using anemometers to measure air flow at supply diffuser and at different operating levels. CFD simulations were according to different scenarios which are to provide a comparison between diffusers and understand indoor airflow regimes. The results considering the flow interaction between diffusers and surroundings showed a detailed visual illustration in CFD simulations and their relation to perceived comfort levels.
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Applying deep learning models to twitter data to detect airport service quality
(Elsevier, 2021-03) Barakat, Huda Mohammed Mohammed; Yeniterzi, R.; Martin-Domingo, Luis; Aviation Management; DOMINGO, Luıs Martın; Barakat, Huda Mohammed Mohammed
Measuring airport service quality (ASQ) is an important process for identifying shortages and suggesting improvements that guide management decisions. This research, introduces a general framework for measuring ASQ using passengers’ tweets about airports. The proposed framework considers tweets in any language, not just in English, to support ASQ evaluation in non-speaking English countries where passengers communicate with other languages. Accordingly, this work uses a large dataset that includes tweets in two languages (English and Arabic) and from four airports. Additionally, to extract passenger evaluations from tweets, our framework applies two different deep learning models (CNN and LSTM) and compares their results. The two models are trained with both general data and data from the aviation domain in order to clarify the effect of data type on model performance. Results show that better performance is achieved with the LSTM model when trained with domain specific data. This study has clear implications for researchers and airport managers aiming to use alternative methods to measure ASQ.
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Automated flow rate control of extrusion for 3D concrete printing incorporating rheological parameters
(Elsevier, 2024-04) Ahi, Oğulcan; Ertunç, Özgür; Bundur, Zeynep Başaran; Bebek, Özkan; Civil Engineering; Mechanical Engineering; ERTUNÇ, Özgür; BUNDUR, Zeynep Başaran; BEBEK, Özkan; Ahi, Oğulcan
The use of inline quality assessment technologies is of great importance in meeting the consistent extrusion requirements of 3D concrete printing (3DCP) applications. This paper presents a system to regulate extrusion speed and maintain the flow rate at a target value during 3DCP processes. The system is based on a new equation that combines printing parameters and the material's rheological properties in the printing process. The proposed control strategy is designed to effectively function with various cement-based mixtures. Validation tests demonstrate that the proposed system can maintain an instantaneous flow rate within a certain range and eventually achieve a constant flow rate. During operation, the flow rate is consistently maintained around the targeted value with an average error rate of 6.7 percent. The flow rate control mechanism shows promise as a reliable and efficient solution for achieving precise and constant flow rates, regardless of the cement mix design used.
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A benchmark for inpainting of clothing images with irregular holes
(Springer, 2020) Kınlı, Osman Furkan; Özcan, Barış; Kıraç, Mustafa Furkan; Computer Science; KINLI, Osman Furkan; KIRAÇ, Mustafa Furkan; Özcan, Barış
Fashion image understanding is an active research field with a large number of practical applications for the industry. Despite its practical impacts on intelligent fashion analysis systems, clothing image inpainting has not been extensively examined yet. For that matter, we present an extensive benchmark of clothing image inpainting on well-known fashion datasets. Furthermore, we introduce the use of a dilated version of partial convolutions, which efficiently derive the mask update step, and empirically show that the proposed method reduces the required number of layers to form fully-transparent masks. Experiments show that dilated partial convolutions (DPConv) improve the quantitative inpainting performance when compared to the other inpainting strategies, especially it performs better when the mask size is 20% or more of the image.
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Blood clotting time measurement using a miniaturized high-frequency ultrasound sensor
(IEEE, 2023) Sobhani, M. R.; Majidi, Negar; Yaralıoğlu, Göksen Göksenin; Electrical & Electronics Engineering; YARALIOĞLU, Göksen Göksenin; Majidi, Negar
This paper demonstrates a novel blood coagulation time measurement methodology that requires as low as 1 microliter of whole blood. The blood sample is placed on the top surface of a fused quartz plate where an ultrasonic transducer is fabricated on the bottom surface. The location of the blood sample is aligned with the transducer; therefore, the reflected acoustic waves from the blood/quartz interface are captured and converted to electrical signals by the transducer. The transducer is made of an 8 μm thick zinc oxide (ZnO) thin film that operates at 400 MHz. The acoustic impedance of blood changes due to the coagulation process. This affects the reflection coefficient and amplitude of the reflected waves from the blood/quartz interface. Thus, the blood coagulation time is determined by monitoring the amplitude of reflected acoustic waves. In the experiments, whole blood was used without any sample preparation. The method was tested using citrated blood with calcium chloride and activated partial thromboplastin (aPTT) reagents. We observed that aPTT coagulation times lengthened from 25 sec. to 47 sec. with the addition of heparin. The proposed method has the potential to be used in a disposable low-cost portable coagulation time measurement cartridge for patient self-testing.
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Centrality and scalability analysis on distributed graph of large-scale e-mail dataset for digital forensics
(IEEE, 2020-12-10) Ozcan, S.; Astekin, Merve; Shashidhar, N. K.; Zhou, B.; Astekin, Merve
Today's digital forensics software tools mostly do not offer automatic analysis methods to reveal evidences among huge amounts of digital files within hard disk images. It is important that finding evidence in digital and cyber forensics investigations as soon as possible by examining hard disk images. E-mails constitute a rich source of information in hard disk images, and they are the most possible data source to obtain an evidence. The analyzers search e-mail files by manually or using traditional methods in order to find an evidence. However, this operation could take a long time due to the size of the e-mail data which can contain a huge number of files and a huge volume of data. This study introduces an end-to-end distributed graph analysis framework for large-scale digital forensic datasets, and evaluates the accuracy of the centrality algorithms and the scalability of the proposed framework in terms of running time performance. The framework is comprised of specific processes to perform pre-processing, graph building, and algorithm activities. An architecture is introduced based on distributed big data techniques. Three different centrality algorithms are implemented to analyze the accuracy of our framework. Further, three implementations are provided to demonstrate the running time performance of our framework. Experiments are performed on Enron e-mail dataset to analyze the centrality algorithms, to evaluate the performance of the framework, and to compare the running times between the traditional approach and our approach. Moreover, the running time performance of the framework is evaluated under various parallelization level. The accuracy of the results is also evaluated and compared between the centrality algorithms. The comparison shows that some certain algorithms provide more accurate results and it is possible to improve the running time by orders of magnitude utilizing our end-to-end distributed graph analysis approach.
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A comparative study for 6D pose estimation of textureless and symmetric objects used in automotive manufacturing industry
(IEEE, 2023) Doruk, Abdullah Enes; Ozkaya, T. E.; Gülez, F.; Uslu, F.; Doruk, Abdullah Enes
6D pose estimation of industrial objects on RGB images has a high potential to accelerate the automation of robotic manipulations in the automotive manufacturing industry. Despite its high potential, this problem has not been adequately addressed in the computer vision community. Main factors leading to under investigation of this problem are industrial objects to be textureless, thin, and symmetrical, which hinder the automatic estimation of their poses from color images. Deep learning models have shown promising results for pose estimation of household objects thanks to availability of large datasets with labels. In contrast to many household objects, there are few datasets for industrial objects with limited representation capacity, which restricts the use of deep models in pose estimation of industrial objects. In this study, we examine the eligibility of deep models on 6D pose estimation of industrial objects used in the automotive manufacturing industry. For this aim, we compare the performance of three deep models, DeepIM, CosyPose, and EPOS. To meet the need for large training dataset of these models, we produce a large synthetic dataset from the CAD data of the industrial objects. We also collect a small real dataset for training and performance evaluation purposes. We find that CosyPose outperforms other methods with a large margin, by showing its potential to solve such a hard problem. We also observe that training models with both synthetic and real images yield the best results.
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Computational and experimental investigation of vibration characteristics of variable unit-cell gyroid structures
(International Center for Numerical Methods in Engineering, 2019) Şimşek, Uğur; Gayir, C.; Kavas, B.; Şendur, Polat; Mechanical Engineering; ŞENDUR, Polat; Şimşek, Uğur
Triply periodic minimal surface (TPMS) based geometries exhibit extraordinary mechanical, thermal, electrical and acoustic properties thanks to their unique topologies. There are various types of structures in the TPMS family. One of the most well-known TPMS structures is the gyroid structure. This paper focuses on the vibrational behavior of a novel sandwiched gyroid structure in terms of their natural frequencies and mode shapes with three different feasible unit sizes at same volume ratio. Powder bed fusion technology is employed to fabricate gyroid porous specimens made of HS188 material. Modal testing is performed to deduce the vibration characteristics of aforementioned cellular structures. Besides the experimental study, the dynamic performance of the considered structures is investigated computationally by performing modal analysis using Finite Element (FE) models. A key challenge facing FE modelling of large scale gyroid structure is computation time and accuracy. For that reason, small size of gyroid lattices are utilized for compression tests in order to extract elastic properties. Then sandwiched gyroid plate is modelled as solid body with calculated elastic properties instead of complex gyroid topology and analyzed. Finally correlation level between experimental and FE results are presented.
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Context based echo state networks for robot movement primitives
(IEEE, 2023) Amirshirzad, Negin; Asada, M.; Öztop, Erhan; Computer Science; ÖZTOP, Erhan; Amirshirzad, Negin
Reservoir Computing, in particular Echo State Networks (ESNs) offer a lightweight solution for time series representation and prediction. An ESN is based on a discrete time random dynamical system that is used to output a desired time series with the application of a learned linear readout weight vector. The simplicity of the learning suggests that an ESN can be used as a lightweight alternative for movement primitive representation in robotics. In this study, we explore this possibility and develop Context-based Echo State Networks (CESNs), and demonstrate their applicability to robot movement generation. The CESNs are designed for generating joint or Cartesian trajectories based on a user definable context input. The context modulates the dynamics represented by the ESN involved. The linear read-out weights then can pick up the context-dependent dynamics for generating different movement patterns for different contexts. To achieve robust movement execution and generalization over unseen contexts, we introduce a novel data augmentation mechanism for ESN training. We show the effectiveness of our approach in a learning from demonstration setting. To be concrete, we teach the robot reaching and obstacle avoidance tasks in simulation and in real-world, which shows that the developed system, CESN provides a lightweight movement primitive representation system that facilitate robust task execution with generalization ability for unseen seen contexts, including extrapolated ones.
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A decomposition-based heuristic for a waste cooking oil collection problem
(Springer, 2020-01-01) Gültekin, Ceren; Ölmez, Ömer Berk; Koyuncu, Burcu Balçık; Ekici, Ali; Özener, Okan Örsan; Industrial Engineering; KOYUNCU, Burcu Balçık; EKİCİ, Ali; ÖZENER, Okan Örsan; Gültekin, Ceren; Ölmez, Ömer Berk
Every year, a tremendous amount of waste cooking oil (WCO) is produced by households and commercial organizations, which poses a serious threat to the environment if disposed improperly. While businesses such as hotels and restaurants usually need to have a contract for their WCO being collected and used as a raw material for biodiesel production, such an obligation may not exist for households. In this study, we focus on designing a WCO collection network, which involves a biodiesel facility, a set of collection centers (CCs), and source points (SPs) each of whom represents a group of households. The proposed locationrouting problem (LRP) determines: (i) the CCs to be opened, (ii) the number of bins to place at each CC, (iii) the assignment of each SP to one of the accessible CCs, and (iv) the vehicle routes to collect the accumulated oil from the CCs. We formulate the problem as a mixed-integer mathematical model and solve it by using commercial solvers by setting a 1-h time limit. We also propose a decompositionbased heuristic and conduct a computational study. Our decomposition algorithm obtains the same or better solutions in 95% of all the test instances compared to the proposed mathematical model.
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Design and development of a torsion-based series elastic actuator with nested encoders for a wearable exoskeleton robot
(IEEE, 2022) Kuru, Alihan; Uğurlu, Regaip Barkan; Bebek, Özkan; Mechanical Engineering; UĞURLU, Regaip Barkan; BEBEK, Özkan; Kuru, Alihan
This paper presents the design of a high torque-to-mass ratio series elastic actuator (SEA) for wearable powered exoskeletons. Nonbackdrivable actuators are ideal for applications that require high torque. Commonly, active exoskeleton robots are powered by actuators that are nonbackdrivable. Due to the high gear ratio, the output mechanical impedance of these actuators is quiet high which renders their force/torque control challenging. To provide torque controllability a custom torsional spring has been produced and placed at the output side of the series elastic actuator. In addition, the measurement of the angular displacement of this elastic element is challenging in terms of mechanical design. To prevent this design challenge a double shaft mechanism was proposed. In this mechanism, the first shaft, which connects the spring and the spring encoder, goes through the second shaft, which is connected to the motor and the motor encoder. This way both encoders are placed on a the same side of the SEA. In addition to explaining this compact spring shaft mechanism, this article presents the results of the cascaded PID controller with a disturbance observer (DoB) applied on the actuator.
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Design methodology of a concentrating solar volumetric receiver
(ASME, 2023) Akba, Tufan; Baker, D.; Mengüç, Mustafa Pınar; Mechanical Engineering; MENGÜÇ, Mustafa Pınar; Akba, Tufan
A volumetric receiver design process is proposed to respond wide range of power, outlet temperature, or mass flow rate needs. In the receiver model, concentrated solar radiation hits the inner surface cavity and heats the gaseous fluid passing through the porous media assembled between the cavity and the insulator. Porous media properties and receiver geometry are coupled in the design process to determine the best possible option. A two-step process starts with a parameter sweep to create a surrogate model. Then, gradient-based design optimization is performed using two different surrogate models to maximize the outlet air temperature for bounded design variables in receiver volume and outer surface temperature constraints. The proposed design process has the advantage of exploring more design options faster using the surrogate model and more accurate results using the base model in the plant-level simulations. The methodology is discussed by comparing the surrogate models and the model validation shows that over 95% accuracy is obtained using both surrogate models. Surrogate-based design optimization is compared as in solution time and the final results are compared with respect to the base receiver model.
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Deterministic neural illumination mapping for efficient auto-white balance correction
(IEEE, 2023) Kınlı, Osman Furkan; Yılmaz, Doğa; Özcan, Barış; Kıraç, Mustafa Furkan; Computer Science; KINLI, Osman Furkan; KIRAÇ, Mustafa Furkan; Yılmaz, Doğa; Özcan, Barış
Auto-white balance (AWB) correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios. This paper presents a novel and efficient AWB correction method that achieves at least 35 times faster processing with equivalent or superior performance on high-resolution images for the current state-of-the-art methods. Inspired by deterministic color style transfer, our approach introduces deterministic illumination color mapping, leveraging learnable projection matrices for both canonical illumination form and AWB-corrected output. It involves feeding high-resolution images and corresponding latent representations into a mapping module to derive a canonical form, followed by another mapping module that maps the pixel values to those for the corrected version. This strategy is designed as resolution-agnostic and also enables seamless integration of any pre-trained AWB network as the backbone. Experimental results confirm the effectiveness of our approach, revealing significant performance improvements and reduced time complexity compared to state-of-the-art methods. Our method provides an efficient deep learning-based AWB correction solution, promising real-time, high-quality color correction for digital imaging applications.
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Developing a national pandemic vaccination calendar under supply uncertainty
(Elsevier, 2024-04) Karakaya, Sırma; Koyuncu, Burcu Balçık; Industrial Engineering; KOYUNCU, Burcu Balçık; Karakaya, Sırma
During the COVID-19 pandemic, many countries faced challenges in developing and maintaining a reliable national pandemic vaccination calendar due to vaccine supply uncertainty. Deviating from the initial calendar due to vaccine delivery delays eroded public trust in health authorities and the government, hindering vaccination efforts. Motivated by these challenges, we address the problem of developing a long-term national pandemic vaccination calendar under supply uncertainty. We propose a novel two-stage mixed integer programming model that considers critical factors such as multiple doses, varying dosing schemes, and uncertainties in vaccine delivery timing and quantity. We present an efficient aggregation-based algorithm to solve this complex problem. Additionally, we extend our model to allow for dynamic adjustments to the vaccine schedule in response to mandatory policy changes, such as modifications to dose intervals, during ongoing vaccinations. We validate our model based on a case study developed by using real COVID-19 vaccination data from Norway. Our results demonstrate the advantages of the proposed stochastic optimization approach and heuristic algorithm. We also present valuable managerial insights through extensive numerical analysis, which can aid public health authorities in preparing for future pandemics.
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Development and 3D spatial calibration of a parallel robot for percutaneous needle procedures with 2D ultrasound guidance
(World Scientific, 2017-12-01) Ahmad, Mirza Awais; Orhan, Sabri Orçun; Yıldırım, Mehmet Can; Bebek, Özkan; Mechanical Engineering; BEBEK, Özkan; Ahmad, Mirza Awais; Orhan, Sabri Orçun; Yıldırım, Mehmet Can
Robotic systems are being applied to medical interventions as they increase the operational accuracy. The proposed autonomous and ultrasound guided 5-DOF parallel robot can achieve such accuracy for needle biopsies, which particularly demand precise needle positioning and insertion. In this paper, the robot's mechanical design, system identifications, and the design of its controller are explained. A torque computed controller with gravity compensation and friction models, yielding a 0.678mm RMS position error for the needle tip, was used. A novel method was used for 3D space calibration of the images for detecting the volume of interest in the biopsy procedure by a multipoint crosswire phantom with parallel threads. The calibration technique had a validation RMS error of 0.03mm.
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Down-conversion emission profile characterisation via camera
(Optica Publishing Group, 2020-12-14) Kuniyil, Hashir Puthiyapurayil; Durak, Kadir; Electrical & Electronics Engineering; DURAK, Kadir; Kuniyil, Hashir Puthiyapurayil
We present a method to improve the brightness and collection efficiency of the spontaneous parametric down-conversion source by monitoring the mode shape using camera and correcting it with collection optics.
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Effects of agent's embodiment in human-agent negotiations
(ACM, 2023-09-19) Çakan, Umut; Keskin, Mehmet Onur; Aydoǧan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Çakan, Umut; Keskin, Mehmet Onur
Human-agent negotiation has recently attracted researchers’ attention due to its complex nature and potential usage in daily life scenarios. While designing intelligent negotiating agents, they mainly focus on the interaction protocol (i.e., what to exchange and how) and strategy (i.e., how to generate offers and when to accept). Apart from these components, the embodiment may implicitly influence the negotiation process and outcome. The perception of a physically embodied agent might differ from the virtually embodied one; thus, it might influence human negotiators’ decisions and responses. Accordingly, this work empirically studies the effect of physical and virtual embodiment in human-agent negotiations. We designed and conducted experiments where human participants negotiate with a humanoid robot in one setting, whereas they negotiate with a virtually embodied replica of that robot in another setting. The experimental results showed that social welfare was statistically significantly higher when the negotiation was held with a virtually embodied robot rather than a physical robot. Human participants took the negotiation more seriously against physically embodied agents and made more collaborative moves in the virtual setting. Furthermore, their survey responses indicate that participants perceived our robot as more humanlike when it is physically embodied.
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Ensemble Learning based on Regressor Chains: A Case on Quality Prediction
(SciTePress, 2019) Demirel, Kenan Cem; Şahin, Ahmet; Albey, Erinç; Industrial Engineering; ALBEY, Erinç; Demirel, Kenan Cem; Şahin, Ahmet
In this study we construct a prediction model, which utilizes the production process parameters acquired from a textile machine and predicts the quality characteristics of the final yarn. Several machine learning algorithms (decision tree, multivariate adaptive regression splines and random forest) are used for prediction. An ensemble method, using the idea of regressor chains, is developed to further improve the prediction performance. Collected data is first segmented into two parts (labeled as “normal” and “unusual”) using local outlier factor method, and performance of the algorithms are tested for each segment separately. It is seen that ensemble idea proves its competence especially for the cases where the collected data is categorized as unusual. In such cases ensemble algorithm improves the prediction accuracy significantly. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
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EPIoT: Enhanced privacy preservation based blockchain mechanism for internet-of-things
(Elsevier, 2024-01) Kashif, Muhammad; Çakmakçı, Kübra Kalkan; Computer Science; ÇAKMAKCİ, Kübra Kalkan; Kashif, Muhammad
With the increasing popularity of the Internet of things (IoT) and giving the end users the opportunity of collecting and analyzing the data by these IoT devices give rise to ultimate privacy concern and is attracting significant attention nowadays. These IoT devices may contain highly sensitive data and data sharing processes which may lead to security and privacy concerns. To surmount these issues, the interaction of IoT with blockchain for a secure transaction is accepted as a candidate solution. However, the innate behavior of blockchain containing complex mathematical proofs and consensus protocol requires high computational power making it less favorable for IoT devices to be connected with. Motivated by a private by-design framework and emphasizing greater control and setting of privacy preferences by the data owner, this paper complements our previous work on privacy preservation in IoT networks. In this paper, we design and propound a complete blockchain-based privacy-preserving framework by deploying service-oriented layers concepts and low computation cryptography, and a less complex consensus protocol to address the privacy concern. Moreover, this paper will unravel the complete end-to-end architecture of IoT-based blockchain purposely build for secure transactions in IoT networks. Security analysis is conducted using AVISPA tool to show that the proposed algorithms attain the desired security goals. This is followed by extensive simulation experiments and ultimate output results cultivating it much favorably for the deployment of IoT applications in real life.
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Exploring scaling efficiency of intel loihi neuromorphic processor
(IEEE, 2023) Uludağ, Recep Buğra; Çaǧdaş, S.; Işler, Y. S.; Şengör, N. S.; Aktürk, İsmail; Computer Science; AKTÜRK, Ismail; Uludağ, Recep Buğra
In this paper, we focus on examining how scaling efficiency evolves in winner-take-all (WTA) network models on Intel Loihi neuromorphic processor, as network-related features such as network size, neuron type, and connectivity scheme change. By analyzing these relationships, our study aims to shed light on the intricate interplay between SNN features and the efficiency of neuromorphic systems as they scale up. The findings presented in this paper are expected to enhance the comprehension of scaling efficiency in neuromorphic hardware, providing valuable insights for researchers and developers in optimizing the performance of large-scale SNNs on neuromorphic architectures.

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