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Item Applications of Deep Learning Techniques to Wood Anomaly Detection(2022) Celik, Yaren; Guney, Selda; Dengiz, Berna; Xu, J; Altiparmak, F.; Hassan, MHA; Marquez, FPGWood 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.Item 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, KMAuscultation 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.Item 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-3003-2022In 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.Item Predicting Bank Return on Equity (ROE) using Neural Networks(2021) Balci, Tolgay; Ogul, HasanMeasuring the performance and profitability of the banking sector, which is the most important part of a country's financial system, is always important. Thanks to the performance measurement, banks can understand the competitive situation, their potential to grow, and the risk, and be more successful in sustaining their lives. This study is considered all state deposit money banks in Turkey. In the literature, using of artificial neural networks (ANN) in banking performance evaluation is rarely studied. Therefore, this paper aims to examine the possibility of ANN utilization for predicting return on equity of Turkey State Deposit Money Banks. The paper compares the accuracy percentages of optimization algorithms of ANN using eleven years quarterly data of six exogenous variables and eight endogenous variables as independent variables and the average return on equity from quarterly of all Turkey state deposit money banks as dependent variable. Given a number of recorded financial parameters, the task is to predict banks' performances using ANN computation methods and to compare prediction results with real results. To evaluate these methods, we built a data set from Banking Regulation and Supervison of Agency, The Banks Association of Turkey and banks' quarterly financial reports. According to all experimental results in optimization models were estimated with above % 80 accuracy. It is determined that the best optimization model is different for each bank.Item Ka Band Far Field Radio Link System Based on OAM Multiplexed Vortex Beams Collimated by a Paraboloidal Reflector(2021) Hizal, Altunkan; Yildiz, HayrullahElectromagnetic 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).Item A New Modeling Approach for Stability of Micro/Nano Bubbles(2021) Dogan, Mustafa; Bunyatova, Ulviya; Ferhanoglu, OnurMicrobubbles 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.Item Anomaly Detection in Smart Home Environments using Convolutional Neural Network(2021) Ercan, Naci Mert; Sert, MustafaThe 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.Item Order Acceptance and Scheduling Problem: A Proposed Formulation and the Comparison with the Literature(2020) Bicakci, Papatya S.; Kara, ImdatIn 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.Item Multiple Service Home Health Care Routing and Scheduling Problem: A Mathematical Model(2020) Dengiz, Asiye Ozge; Atalay, Kumru Didem; Altiparmak, FulyaThe home health care routing and scheduling problem (HHCRSP) is an extension of the vehicle routing problem (VRP) that are scheduled and routed to perform a wide range of health care services. Nurses, doctors and/or caregivers provide these services at patients' home. In this study, a mathematical model for HHCRSP is presented. The model is extended to take into account additional characteristics and/or constraints based on specific services, patient needs. In the home health care (HHC) problems, services that must be performed simultaneously or within a convinced time are undoubtedly very important. Thus, we consider several numbers of services, skill requirements for the care workers and time windows. Generally, the main aim of the HHC problems is minimizing the travelling distance as well as maximizing the patients' satisfaction. Thus, the model in this study contains both of these objectives taking into account several measurements.Item A Component Based Model Developed for Machine Tool Selection Decisions(2020) Ic, Yusuf Tansel; Yurdakul, Mustafa; AAI-1081-2020Machine tools are widely used in manufacturing sectors; such as automotive industry, metal cutting industry, aerospace industry etc. Purchase of a machine tool is a long-term capital investment decision and requires a high initial investment cost. Machine tool producers offer a wide-ranging types and models of machine tools. On the other hand, expectations and requirements of the manufacturing companies differ depending on the parts produced and their strategic objectives. High stiffness, rigidity, metal cutting performance, surface finish and low tolerance range are common expectations from machine tools. This paper aims to develop a technical evaluation model to help manufacturing companies in their machine tool purchasing decisions. In the proposed model, first components used in machine tools are analyzed and based on this analysis a technical evaluation model is developed. The application of the developed model is illustrated by making a selection among nine different machine tool alternatives.
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