Graduate School of Engineering and Science
Permanent URI for this communityhttps://hdl.handle.net/10679/8952
Browse
Browsing by Issue Date
Now showing 1 - 20 of 48
- Results Per Page
- Sort Options
ArticlePublication Metadata only 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 CanRobotic 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.Conference paperPublication Metadata only An integrated design approach for a series elastic actuator: Stiffness formulation, fatigue analysis, thermal management(IEEE, 2017-12-22) Yıldırım, Mehmet Can; Şendur, Polat; Bilgin, Onur; Gülek, Berk; Yapıcı, Güney Güven; Uğurlu, Regaip Barkan; Mechanical Engineering; YAPICI, Güney Güven; UĞURLU, Regaip Barkan; ŞENDUR, Polat; Yıldırım, Mehmet Can; Bilgin, Onur; Gülek, BerkThis paper presents an integrated mechanical design approach for the long-Term and repetitive use of series elastic actuators (SEAs). Already, computational models for series elastic actuator design have been developed in order to address the challenging weight and volume targets. However, an integrated design method in which the coupling effects between various interacting requirements that are explored at every stage of the design cycle does not exist. In particular, the interactions between the torsional stiffness, strength, fatigue life and thermal performance are not analyzed in-depth. To this end, we propose a comprehensive design approach in which the aforementioned requirements (FEA, stiffness formulation, fatigue analysis, and thermal management) are integrated in a complementary manner. Computer-Aided analyses and experimental results verified the effectiveness of our design approach. The proposed approach is employed to manufacture our SEA module CoEx-SEA.Conference paperPublication Metadata only 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, UğurTriply 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.Conference paperPublication Metadata only 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, AhmetIn 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 reservedConference paperPublication Metadata only International roaming traffic optimization with call quality(SciTePress, 2019) Şahin, Ahmet; Demirel, Kenan Cem; Albey, Erinç; Gürsun, Gonca; Industrial Engineering; ALBEY, Erinç; Şahin, Ahmet; Demirel, Kenan Cem; Gürsun, GoncaIn this study we focus on a Steering International Roaming Traffic (SIRT) problem with single service that concerns a telecommunication’s operators’ agreements with other operators in order to enable subscribers access services, without interruption, when they are out of operators’ coverage area. In these agreements, a subscriber’s call from abroad is steered to partner operator. The decision for which each call will be forwarded to the partner is based on the user’s location (country/city), price of the partner operator for that location and the service quality of partner operator. We develop an optimization model that considers agreement constraints and quality requirements while satisfying subscribers demand over a predetermined time interval. We test the performance of the proposed approach using different execution policies such as running the model once and fixing the roaming decisions over the planning interval or dynamically updating the decisions using a rolling horizon approach. We present a rigorous trade off analysis that aims to help the decision maker in assessing the relative importance of cost, quality and ease of implementation. Our results show that steering cost is decreased by approximately 25% and operator mistakes are avoided with the developed optimization model while the quality of the steered calls is kept above the base quality level.ReviewPublication Open Access Critical review of the parameters affecting the effectiveness of moisture absorption treatments used for natural composites(MDPI AG,, 2019) Al-Maharma, Ahmad Yousef Mohammad; Al-Huniti, N.; Al-Maharma, Ahmad Yousef MohammadNatural composites can be fabricated through reinforcing either synthetic or bio-based polymers with hydrophilic natural fibers. Ultimate moisture absorption resistance at the fiber–matrix interface can be achieved when hydrophilic natural fibers are used to reinforce biopolymers due to the high degree of compatibility between them. However, the cost of biopolymers is several times higher than that of their synthetic counterparts, which hinders their dissemination in various industries. In order to produce economically feasible natural composites, synthetic resins are frequently reinforced with hydrophilic fibers, which increases the incompatibility issues such as the creation of voids and delamination at fiber–matrix interfaces. Therefore, applying chemical and/or physical treatments to eliminate the aforementioned drawbacks is of primary importance. However, it is demonstrated through this review study that these treatments do not guarantee a sufficient improvement of the moisture absorption properties of natural composites, and the moisture treatments should be applied under the consideration of the following parameters: (i) type of hosting matrix; (ii) type of natural fiber; (iii) loading of natural fiber; (iv) the hybridization of natural fibers with mineral/synthetic counterparts; (v) implantation of nanofillers. Complete discussion about each of these parameters is developed through this study.Book ChapterPublication Metadata only Relaying techniques for free space optical communications(Institution of Engineering and Technology, 2019-01-01) Aminikashani, Mohammadreza; Uysal, Murat; Electrical & Electronics Engineering; UYSAL, Murat; Aminikashani, MohammadrezaDespite the major advantages of FSO technology and variety of its application areas, its widespread use has been hampered by its rather disappointing link reliability particularly in long ranges due to atmospheric turbulence-induced fading. Relay-assisted systems have been introduced as an effective method to extend coverage and mitigate the effects of fading in FSO links. In this chapter, we have analyzed and investigated the outage performance of relay-assisted FSO links with AF and DF relays. For serial relaying, it has been demonstrated that the outage probability is minimized when the consecutive nodes are placed equidistant along the path from the source to the destination. For parallel relaying, it has been shown that all of the relays should be located at the same place (along the direct link between the source and the destination) closer to the source and the exact location of this place depends on the system and channel parameters. Multi-hop parallel relaying which is the combined use of serial (multi-hop) and parallel relaying for FSO mesh networks has been also studied. Our analysis yields that multi-hop parallel relaying smartly exploits the distance dependency of the fading variance in FSO systems and bring substantial improvements with respect to standalone uses of multi-hop and parallel relaying. As an alternative way of implementation, all-optical relaying has been also considered. Unlike the earlier relaying schemes, the signals are processed in optical domain and therefore the requirement of OE and EO conversions is avoided. Comparisons between conventional and all-optical relaying demonstrate that the latter presents a favorable trade-off between complexity and performance and can be used as a low-complexity solution.ArticlePublication Metadata only Provenance aware run-time verification of things for self-healing Internet of Things applications(Wiley, 2019-02-10) Aktas, M. S.; Astekin, Merve; Astekin, MerveWe propose a run-time verification mechanism of things for self-healing capability in the Internet of Things domain. We discuss the software architecture of the proposed verification mechanism and its prototype implementations. To identify faulty running behavior of things, we utilize a complex event processing technique by applying rule-based pattern detection on the events generated real time. For events, we use a descriptor metadata of the measurements (such as CPU usage, memory usage, and bandwidth usage) taken from Internet of Things devices. To understand the usability and effectiveness of the proposed mechanism, we developed prototype applications using different event processing platforms. We test the prototype implementations for performance and scalability under increasing message rates. The results are promising because the processing overhead of the proposed verification mechanism is negligible.ArticlePublication Open Access Depression screening from voice samples of patients affected by parkinson’s disease(S. Karger AG, 2019-05-01) Özkanca, Yasin Serdar; Öztürk, M. G.; Ekmekci, Merve Nur; Atkins, D. C.; Demiroğlu, Cenk; Ghomi, R. H.; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Özkanca, Yasin Serdar; Ekmekci, Merve NurDepression is a common mental health problem leading to significant disability worldwide. It is not only common but also commonly co-occurs with other mental and neurological illnesses. Parkinson's disease (PD) gives rise to symptoms directly impairing a person's ability to function. Early diagnosis and detection of depression can aid in treatment, but diagnosis typically requires an interview with a health provider or a structured diagnostic questionnaire. Thus, unobtrusive measures to monitor depression symptoms in daily life could have great utility in screening depression for clinical treatment. Vocal biomarkers of depression are a potentially effective method of assessing depression symptoms in daily life, which is the focus of the current research. We have a database of 921 unique PD patients and their self-assessment of whether they felt depressed or not. Voice recordings from these patients were used to extract paralinguistic features, which served as inputs to machine learning and deep learning techniques to predict depression. The results are presented here, and the limitations are discussed given the nature of the recordings which lack language content. Our models achieved accuracies as high as 0.77 in classifying depressed and nondepressed subjects accurately using their voice features and PD severity. We found depression and severity of PD had a correlation coefficient of 0.3936, providing a valuable feature when predicting depression from voice. Our results indicate a clear correlation between feeling depressed and PD severity. Voice may be an effective digital biomarker to screen for depression among PD patients.Conference paperPublication Open Access Two-part bio-based self-healing repair agent for cement-based mortar(International Center for Numerical Methods in Engineering, 2020) Tezer, Mustafa Mert; Bundur, Zeynep Başaran; Civil Engineering; BUNDUR, Zeynep Başaran; Tezer, Mustafa MertFactors affecting durability of concrete structures are generally associated with each other. Due to its brittle nature, concrete can crack under stress and these cracks are one of the main reasons for a decrease in service life in concrete structures. Therefore, it is crucial to detect and recover microcracks, then to repair them as they were developed to wider cracks. Recent research in the field of concrete materials suggested that it might be possible to develop a smart cement-based material that is capable of remediate cracks by triggering biogenic calcium carbonate (CaCO3) precipitaton. This paper summarizes a study undertaken to investigate the self-healing efficiency of Sporosarcina pasteurii (S. pasteurii) cells immobilized on both diatomaceous earth and pumice, to remediate flexural cracks on mortar in early ages (28 days after mixing). To obtain a two-phase bio additive, half of the minerals were saturated with a nutrient medium consisting of urea, corn-steep liqueur(CSL) and calcium acetate and the cells with immobilized to the other half without nutrients. Screening of the healing process was done with ultrasonic pulse velocity (UPV) testing and stereomicroscopy. With this approach, the cracks on mortar surface were sealed and the water absorption capacity of the so-called self-healed mortar decreased compared to its counterpart cracked mortar samples.Conference paperPublication Metadata only Uncertainty-aware deep classifiers using generative models(Association for the Advancement of Artificial Intelligence, 2020) Şensoy, Murat; Kaplan, L.; Cerutti, F.; Saleki, Maryam; Computer Science; ŞENSOY, Murat; Saleki, MaryamDeep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods.Conference paperPublication Metadata only 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, BarışFashion 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.Conference paperPublication Metadata only Numerical analysis of solar radiation effects at indoors with internal partitions and external solar shades(International Solar Energy Society, 2020) Yelekci, Ali Can; Keskin, Cem; Mengüç, Mustafa Pınar; Mechanical Engineering; MENGÜÇ, Mustafa Pınar; Yelekci, Ali Can; Keskin, CemA numerical study is conducted to couple natural convection in an office space with thermal radiation due to solar radiation. The study specifically investigates the effect of partitions located between desks of the office space to develop a tool-box to determine the effect of windows on thermal and visual comfort of occupants. Three different partition cases (according to the aspect ratio of the partition to the ceiling height, which are 0.3, 0.5 and 1.0) were studied. Moreover, the effects of different designs of solar shades in front of windows were investigated. All walls other than the facade of the enclosure are assumed adiabatic, and the enclosure has a single window, which acts as a thermal radiative heat source. All surfaces are assumed to be gray-diffuse surfaces for calculation of thermal radiation. The solar radiation is analyzed for a perfect sunny day with both diffuse and direct sunlight, and for an overcast day with only diffuse sunlight. Based on the choice of partitions geometry, solar shade aspect ratios and the weather conditions, variations on the surface temperature distribution inside the office are analyzed.Book ChapterPublication Metadata only 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 BerkEvery 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.Conference paperPublication Metadata only Photon statistics effects on a QRNG of vacuum fluctuations(Optica Publishing Group, 2020-09-14) Dandaşi, Abdulrahman; Özel, Helin; Durak, Kadir; Electrical & Electronics Engineering; DURAK, Kadir; Dandaşi, Abdulrahman; Özel, HelinOptical scattering enhances randomness characteristics, increases the chaotic behavior of coherent sources, broadens the distribution of photon statistics and makes it super-Poissonian which allows faster sampling compared to Poissonian.ArticlePublication Metadata only 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, MerveToday'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.Conference paperPublication Metadata only 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 PuthiyapurayilWe 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.Conference paperPublication Metadata only PYNQ-based rapid FPGA implementation of quantum key distribution(IEEE, 2021) Bilgin, Yiğit; Tesfay, Shewıt Weldu; İpek, Seçkin; Uğurdağ, Hasan Fatih; Durak, Kadir; Gören, S.; Electrical & Electronics Engineering; UĞURDAĞ, Hasan Fatih; DURAK, Kadir; Bilgin, YiğitIn this paper, we present a real-time Quantum Key Distribution (QKD) implementation on Field Programmable Gate Arrays (FPGAs) for secure communication. We propose a novel methodology with a Python-based programming interface for rapid development on FPGA. Our methodology revolves three phases of development. In the first phase, a reference model of an entangled photon source and the proposed QKD system are developed in Python. Next, the reference model is passed through a thorough verification phase. In the second phase, the reference model is implemented on the Processing System (PS) part of the FPGA. Finally in the third phase, the computationally intensive part of the QKD architecture is off-loaded on to the Programmable Logic (PL) part of the FPGA for acceleration. We employ PYNQ framework in our QKD development and successfully combine the convenience of Python productivity with FPGA based acceleration.ArticlePublication Metadata only 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 MohammedMeasuring 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.Conference paperPublication Open Access Validation and comparison of 2D and 3D numerical simulations of flow in simplex nozzles(Europe, Institute for Liquid Atomization and Spray Systems, ILASS, 2021-08-31) Bal, M.; Kayansalçik, Gökhan; Ertunç, Özgür; Böke, Y. E.; Mechanical Engineering; ERTUNÇ, Özgür; Kayansalçik, GökhanNumerical simulations of pressure swirl atomizers are computationally expensive due to transient and multiphase flow behavior. In this study, 2D and 3D VOF simulations are performed for a geomerty which has high swirl chamber length-to-diameter ratio of 1.33. discharge coefficient (CD) and spray angle values are compared to the experimental data. Moreover, a benchmark study is conducted between 2D and 3D methods in terms of accuracy, computational cost and flow variables such as orifice exit axial and tangential velocity. The simulations are performed using a hybrid RANS-LES approach, IDDES model. It is observed that 2D simulation has lower accuracy in the validation parameters such as discharge coefficient and spray angle as compared to the 3D simulation. The main reason for 2D simulation inaccuracy might be the tangential port inlet effects and wrong estimation of the loss of swirl inside the swirl chamber. On the other hand, 2D simulations have approximately 1000 times lower computational cost than 3D simulations.
- «
- 1 (current)
- 2
- 3
- »