Scopus İndeksli Yayınlar Koleksiyonu

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    Applications of Deep Learning Techniques to Wood Anomaly Detection
    (2022) Celik, Yaren; Guney, Selda; Dengiz, Berna; Xu, J; Altiparmak, F.; Hassan, MHA; Marquez, FPG
    Wood products and structures have an important place in today's industry. They are widely used in many fields. However, there are various difficulties in production systems where wood raw material is under many processes. Some difficulty and complexity of production processes result in high variability of raw materials such as a wide range of visible structural defects that must be checked by specialists on line or of line. These issues are not only difficult and biased in manual processes, but also less effective and misleading. To overcome the drawbacks of the manual quality control processes, machine vision-based inspection systems are in great of interest recently for quality control applications. In this study, the wood anomaly has been detected by using deep learning. As it will be a distinction-based method on image processing, the Convolution Neural Network (CNN), which is one of the most suitable methods, has been used for anomaly detection. In addition, it will be tried to obtain the most suitable one among different CNN architectures such as ShuffleNet, AlexNet, GoogleNet for the problem. MobileNet, SqueezeNet, GoogleNet, ShuffleNet among considered methods show promising results in classifying normal and abnormal wood products.
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    Detecting COVID-19 from Respiratory Sound Recordings with Transformers
    (2022) Aytekin, Idil; Dalmaz, Onat; Ankishan, Haydar; Saritas, Emine U.; Bagci, Ulas; Cukur, Tolga; Celik, Haydar; Drukker, K; Iftekharuddin, KM
    Auscultation is an established technique in clinical assessment of symptoms for respiratory disorders. Auscultation is safe and inexpensive, but requires expertise to diagnose a disease using a stethoscope during hospital or office visits. However, some clinical scenarios require continuous monitoring and automated analysis of respiratory sounds to pre-screen and monitor diseases, such as the rapidly spreading COVID-19. Recent studies suggest that audio recordings of bodily sounds captured by mobile devices might carry features helpful to distinguish patients with COVID-19 from healthy controls. Here, we propose a novel deep learning technique to automatically detect COVID-19 patients based on brief audio recordings of their cough and breathing sounds. The proposed technique first extracts spectrogram features of respiratory recordings, and then classifies disease state via a hierarchical vision transformer architecture. Demonstrations are provided on a crowdsourced database of respiratory sounds from COVID-19 patients and healthy controls. The proposed transformer model is compared against alternative methods based on state-of-the-art convolutional and transformer architectures, as well as traditional machine-learning classifiers. Our results indicate that the proposed model achieves on par or superior performance to competing methods. In particular, the proposed technique can distinguish COVID-19 patients from healthy subjects with over 94% AUC.
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    Understanding the Effect of Assignment of Importance Scores of Evaluation Criteria Randomly in the Application of DOE-TOPSIS in Decision Making
    (2019) Ic, Yusuf Tansel; Yurdakul, Mustafa; 0000-0001-9274-7467; AGE-3003-2022
    In conventional applications of hybrid DoE-TOPSIS technique in decision making problems, full factorial design layouts are generally used because of their ability to measure the effects of all possible combinations for evaluation factors. In a typical application, for a design layout, a number of replications are generated by assigning different sets of relative importance scores for evaluation factors. A TOPSIS score is then obtained for each experiment and replication pair. Regression analysis is finally applied to obtain a relationship with inputs (values of evaluation factors) and outputs (alternatives' TOPSIS meta-model scores). The key in conventional application of hybrid DoE-TOPSIS technique is generation of relative importance scores. Each set of scores can be assigned by a decision maker or generated randomly. This paper aims to determine whether using either of the two methods in determination of relative importance scores makes any difference in the ranking orders of alternatives.
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    Ka Band Far Field Radio Link System Based on OAM Multiplexed Vortex Beams Collimated by a Paraboloidal Reflector
    (2021) Hizal, Altunkan; Yildiz, Hayrullah
    Electromagnetic vortex-waves (VW) have linear azimuthal-phase, orthogonality in azimuth and orbital-angular-momentum (OAM). The VW-pattern has a null along the vorticity-axis and the cone-half-angle (CHA) and the beam-width (BW) expands with the OAM mode number p. Here, we collimated all p-VW-beams into a radiation cone (Rcon) with a small CHA and BW using a paraboloidal reflector (PaR) fed by a VW uniform-circular-array (UCA). We multiplexed all the transmitted (TX) +/- p-modes, each modulated by a 16QAM symbol-vector. We receive (RX) the TX-signal by p(max) number of nonvortex PaR antennas placed on a small arc of the Rcon. The RX-signal is cast into the standard discrete-Fourier-transform (DFT) format, using the beam-collimation, the azimuthal-orthogonality and zero-padding. The demultiplexing is performed by IDFT. The UCA is designed at Ka-band using circular microstrip-patch-antennas (msPA). The +/- p-modes are TX'ed by orthogonally-polarized separate msPA's. The effects of coupling of +/- p-modes, the calibration inaccuracies and signal-to-noise-ratio (SNR) are simulated by the Monte-Carlo method. It was found that the SNR is very high and the far-field radio-link is feasible. The bit rate of the present OAM-16QAM radio-link is increased by a factor of 2 p(max).
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    A New Modeling Approach for Stability of Micro/Nano Bubbles
    (2021) Dogan, Mustafa; Bunyatova, Ulviya; Ferhanoglu, Onur
    Microbubbles and nanobubbles have several characteristics that are comparable with millimeter- and centimeter-sized bubbles. These characteristics are their small size, which results in large surface area and high bioactivity, low rising velocity, decreased friction drag, high internal pressure, large gas dissolution capacity, negatively charged surface, and ability to be crushed and form free radicals. Controlling and modeling fundamental properties such as nucleation and of the dynamics of these bubbles is key to successfully exploiting their potential in the growing number of applications such as biomedical diagnosis and therapy, antimicrobial in aquaculture, environment, engineering, stock raising and marine industry. Laser-generated bubble dimensions can be characterized with an optical setup employing a high power continuous wave green laser for bubble generation. In this work, non-resonant, self-excited due to structurally nonlinear properties of the hydrogel, bubble formation was modeled as functions of well-controlled parameters of the colloidal media that is multi-layered and anisotropic, engineered uniquely. Copyright (C) 2021 The Authors.
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    Anomaly Detection in Smart Home Environments using Convolutional Neural Network
    (2021) Ercan, Naci Mert; Sert, Mustafa
    The use of smart devices in home environments has been increasing in recent years. The wireless connection of these devices to the internet enables smart homes to be built with less cost and hence, recognition of activities in home environments and the detection of possible anomalies in activities is important for several applications. In this study, we propose a new method based on the changepoint representation of sensor data and variable-length windowing for the recognition of abnormal activities. We present comparative analyses with different representations to demonstrate the efficacy of the proposed scheme. Our results on the WSU performance dataset show that, the use of variable-length windowing improves the anomaly detection performance in comparison to fixed-length windowing.
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    Order Acceptance and Scheduling Problem: A Proposed Formulation and the Comparison with the Literature
    (2020) Bicakci, Papatya S.; Kara, Imdat
    In classical scheduling problem, it is assumed that all orders must be processed. In the order acceptance and scheduling (OAS) problem, some orders are rejected due to limited capacity. In make-to-order production environment, in which the OAS problem occurs, accepting all orders may cause overloads, delay in deliveries and unsatisfied customers. Oguz et al. (2010) introduced the OAS problem with sequence-dependent setup times and release dates. In this paper, we propose a new mixed integer programming formulation with O(n(2)) decision variables and O(n2) constraints for the same problem. We conduct a computational analysis comparing the performance of our formulation with Oguz et al. (2010) formulation. We use the benchmark instances, which are available in the literature. We observe that our formulation can solve all the instances up to 50 orders in a reasonable time, while Oguz et al. (2010) formulation can solve only the instances with 10 orders in the same time limit.
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    Objective Pain Assessment Using Vital Signs
    (2020) Erdogan, Burak; Ogul, Hasan
    Pain is considered as an emotional experience and unrestful feeling associated with tissue damage. The feeling of pain occurs when the interpretation starts in brain; as a signal is sent through nerve fiber to the brain. Pain allows the body to prevent further tissue damage. Since there are different ways of expressing and feeling pain, the experience of pain is unique for everybody. In this respect, objective pain assessment is a key step and a major challenge in proper management of pain in different individuals. In this study, we offer a computational solution for objective assessment of pain using vital signs. To this end, we have reported the prediction for existence of pain by calculating the performances of several computational methods that take the sequence of vital signs acquired until pain observation as input. We claim that the use of computational intelligence methods can encourage computer-aided monitoring of pain in a hospitalized environment to a certain degree. (C) 2020 The Authors. Published by Elsevier B.V.
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    Daphnet Freezing Recognition with Gait Data by Using Machine Learning Algorithms
    (2020) Guney, Selda; Boluk, Busra
    The aim of this study was to test the success of the data set obtained by a wearable health assistant developed for the symptom of freezing (FOG) in gait of Parkinson's patients and to increase the success of the system. The system was tested with different machine learning methods to measure the success of the wearable health assistant system. For all patients (ten patients), the highest success value was obtained and the mean sensitivity and specificity values of the system were calculated and compared with the results obtained in the literature review. In the literature, mean sensitivity and specificity were 73.1% and 81.6%, respectively; In this study, mean sensitivity and specificity were 91.9% and 71.14%, respectively. In order to better analyze the success of the system, two patients with successful and unsuccessful results were selected for the data set in line with the results obtained in the literature review. The success of the system was tested by using different machine learning methods on the data sets of two patients. Finally, the successes obtained by feature extraction methods were tried to be increased. Among the different machine learning methods on the data sets used for patient 8 and patient 3, the most successful method was obtained by combining the models (ensemble). The highest achievement value obtained by attribute extraction methods was obtained when PCA was applied. However, the success value obtained with raw data could not be increased. All results are tabulated and presented.
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    Wi-Fi Based Indoor Positioning System with Using Deep Neural Network
    (2020) Guney, Selda; Erdogan, Alperen; Aktas, Melih; Ergun, Mert
    Indoor positioning is one of the major challenges for the future large-scale technologies. Nowadays, it has become an attractive research subject due to growing demands on it. Several algorithms and techniques have been developed over the decades. One of the most cost-effective technique is Wi-Fi-based positioning systems. This technique is infrastructure-free and able to use existing wireless access points in public or private areas. These systems aim to classify user's location according to pre-defined set of grids. However, Wi-Fi signals could be affected by interference, blockage of walls and multipath effect which increases error of classification. In this study Deep Neural Networks and conventional machine learning classifiers are utilized to classify 22 squared grids which represent locations. Five primary Wireless Access Points (WAPs) were mounted indoor environment and 177 secondary WAPs are observed by Wi-Fi module. Dataset was created with using five primary and 177 secondary WAPs. The performance of proposed method was tested using Deep Neural Networks and machine learning classifiers. The results show that Deep Neural Network present the best performance as compared to machine learning classifiers. 95.45% accuracy was achieved by using five primary WAPs and 97.27% accuracy was achieved by using five primary and 177 secondary WAPs together for Deep Neural Network.