Graduate School of Engineering and Science

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

Browse

Now showing 1 - 20 of 48

Metadata only
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.
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 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.
Metadata only
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.
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, 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.
Metadata only
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.
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, 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.
Metadata only
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.
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, 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.
Metadata only
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.
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 Mohammad
Natural 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.
Open Access
Data fusion analysis and synthesis framework for improving disaster situation awareness
(MDPI, 2023-09) Aksit, M.; Say, Hanne; Eren, Mehmet Arda; de Camargo, V. V.; Say, Hanne; Eren, Mehmet Arda
To carry out required aid operations efficiently and effectively after an occurrence of a disaster such as an earthquake, emergency control centers must determine the effect of disasters precisely and and in a timely manner. Different kinds of data-gathering techniques can be used to collect data from disaster areas, such as sensors, cameras, and unmanned aerial vehicles (UAVs). Furthermore, data-fusion techniques can be adopted to combine the data gathered from different sources to enhance the situation awareness. Recent research and development activities on advanced air mobility (AAM) and related unmanned aerial systems (UASs) provide new opportunities. Unfortunately, designing these systems for disaster situation analysis is a challenging task due to the topological complexity of urban areas, and multiplicity and variability of the available data sources. Although there are a considerable number of research publications on data fusion, almost none of them deal with estimating the optimal set of heterogeneous data sources that provide the best effectiveness and efficiency value in determining the effect of disasters. Moreover, existing publications are generally problem- and system-specific. This article proposes a model-based novel analysis and synthesis framework to determine the optimal data fusion set among possibly many alternatives, before expensive implementation and installation activities are carried out.
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 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.
Open Access
Deep learning-based expressive speech synthesis: a systematic review of approaches, challenges, and resources
(Springer, 2024-02-12) Barakat, Huda Mohammed Mohammed; Turk, O.; Demiroğlu, Cenk; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Barakat, Huda Mohammed Mohammed
Speech synthesis has made significant strides thanks to the transition from machine learning to deep learning models. Contemporary text-to-speech (TTS) models possess the capability to generate speech of exceptionally high quality, closely mimicking human speech. Nevertheless, given the wide array of applications now employing TTS models, mere high-quality speech generation is no longer sufficient. Present-day TTS models must also excel at producing expressive speech that can convey various speaking styles and emotions, akin to human speech. Consequently, researchers have concentrated their efforts on developing more efficient models for expressive speech synthesis in recent years. This paper presents a systematic review of the literature on expressive speech synthesis models published within the last 5 years, with a particular emphasis on approaches based on deep learning. We offer a comprehensive classification scheme for these models and provide concise descriptions of models falling into each category. Additionally, we summarize the principal challenges encountered in this research domain and outline the strategies employed to tackle these challenges as documented in the literature. In the Section 8, we pinpoint some research gaps in this field that necessitate further exploration. Our objective with this work is to give an all-encompassing overview of this hot research area to offer guidance to interested researchers and future endeavors in this field.
Open Access
Deep transformer-based asset price and direction prediction
(IEEE, 2024) Gezici, Abdul Haluk Batur; Sefer, Emre; Computer Science; SEFER, Emre
The field of algorithmic trading, driven by deep learning methodologies, has garnered substantial attention in recent times. Within this domain, transformers, convolutional neural networks, and patch embedding-based techniques have emerged as popular choices within the computer vision community. Here, inspired by the latest cutting-edge computer vision methodologies and the existing work showing the capability of image-like conversion for time-series datasets, we apply more advanced transformer-based and patch-based approaches for predicting asset prices and directional price movements. The employed transformer models include Vision Transformer (ViT), Data Efficient Image Transformers (DeiT), and Swin. We use ConvMixer for a patch embedding-based convolutional neural network architecture without a transformer. Our tested transformer-based and patch-based methodologies aim to predict asset prices and directional movements using historical price data by leveraging the inherent image-like properties within the historical time-series dataset. Before the implementation of attention-based architectures, the historical time series price dataset is transformed into two-dimensional images. This transformation is facilitated through the incorporation of various common technical financial indicators, each contributing to the data for a fixed number of consecutive days. Consequently, a diverse set of two-dimensional images is constructed, reflecting various dimensions of the dataset. Subsequently, the original images depicting market valleys and peaks are annotated with labels such as Hold, Buy, or Sell. According to the experiments, trained attention-based models consistently outperform the baseline convolutional architectures, particularly when applied to a subset of frequently traded Exchange-Traded Funds (ETFs). This better performance of attention-based architectures, especially ViT, is evident in terms of both accuracy and other financial evaluation metrics, particularly during extended testing and holding periods. These findings underscore the potential of transformer-based approaches to enhance predictive capabilities in asset price and directional forecasting. Our code and processed datasets are available at https://github.com/seferlab/price_transformer.
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 Nur
Depression 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.
Metadata only
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.
Metadata only
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.
Metadata only
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.
Metadata only
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.
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 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.

Browse

Browsing by Title