Performance-Aware High-Performance Computing for Remote Sensing Big Data Analytics

The incredible increase in the volume of data emerging along with recent technological developments has made the analysis processes which use traditional approaches more difficult for many organizations. Especially applications involving subjects that require timely processing and big data such as satellite imagery, sensor data, bank operations, web servers, and social networks require efficient mechanisms for collecting, storing, processing, and analyzing these data. At this point, big data analytics, which contains data mining, machine learning, statistics, and similar techniques, comes to the help of organizations for end-to-end managing of the data. In this chapter, we introduce a novel high-performance computing system on the geo-distributed private cloud for remote sensing applications, which takes advantages of network topology, exploits utilization and workloads of CPU, storage, and memory resources in a distributed fashion, and optimizes resource allocation for realizing big data analytics efficiently

A comparison of clinical, laboratory and chest CT findings of laboratory-confirmed and clinically diagnosed COVID-19 patients at first admission

PurposeThis study aims to identify chest computed tomography (CT) characteristics of coronavirus disease 2019 (COVID-19), investigate the association between CT findings and laboratory or demographic findings, and compare the accuracy of chest CT with reverse transcription-polymerase chain reaction (RT-PCR).MethodsOverall, 120 of 159 consecutive cases isolated due to suspected COVID-19 at our hospital between 17 and 25 March 2020 were included in this retrospective study. All patients underwent both chest CT and RT-PCR at first admission. The patients were divided into two groups: laboratory-confirmed COVID-19 and clinically diagnosed COVID-19. Clinical findings, laboratory findings, radiologic features and CT severity index (CT-SI) of the patients were noted. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of chest CT were calculated for the diagnosis of COVID-19, using RT-PCR as reference.ResultsThe laboratory-confirmed and clinically diagnosed COVID-19 groups consisted of 69 (M/F 43/26, mean age 50.9±14.0 years) and 51 patients (M/F 24/27, mean age 50.9±18.8 years), respectively. Dry cough (62.3% vs. 52.9%), fever (30.4% vs. 25.5%) and dyspnea (23.2% vs. 27.5%) were the most common admission symptoms in the laboratory-confirmed and clinically diagnosed COVID-19 groups, respectively. Bilateral multilobe involvement (83.1% vs. 57.5%), peripheral distribution (96.9% vs. 97.5%), patchy shape (75.4% vs. 70.0%), ground-glass opacities (GGO) (96.9% vs. 100.0%), vascular enlargement (56.9% vs. 50.0%), intralobular reticular density (40.0% vs. 40.0%) and bronchial wall thickening (27.7% vs. 45.0%) were the most common CT findings in the laboratory-confirmed and clinically diagnosed COVID-19 subgroups, respectively. Except for the bilateral involvement and white blood cell (WBC) count, no difference was found between the clinical, laboratory, and parenchymal findings of the two groups. Positive correlation was found between CT-SI and, lactate dehydrogenase (LDH) and C-reactive protein (CRP) values in the laboratory-confirmed COVID-19 subgroup. Chest CT and RT-PCR positivity rates among patients with suspected COVID-19 were 87.5% (105/120) and 57.5% (69/120), respectively. The sensitivity, specificity, PPV, NPV and accuracy rates of chest CT were determined as 94.2% (95% confidence interval [CI], 85.8–98.4), 21.57% (95% CI, 11.3–35.3), 61.90% (95% CI, 58.2–65.5), 73.3% (95% CI, 48.2–89.1) and 63.3% (95% CI, 54.1–71.9), respectively.ConclusionChest CT has high sensitivity and low specificity in the diagnosis of COVID-19. The clinical, laboratory, and CT findings of laboratory-confirmed and clinically diagnosed COVID-19 patients are similar

Security aware routing protocol for wireless broadband mobile networks

Bu çalışmada kablosuz genişbant mobil ağlar için güvenlik bilinçli zeki bir yönlendirme protokolü geliştirilmiştir. Geliştirilen protokol proaktif ve deterministik olmayan yönlendirme algoritmasına sahiptir. Genetik algoritma en kısa yolu bulmak ve bulanık mantık yolların güvenlik seviyelerini dilsel olarak ifade etmek amacıyla kullanılmaktadır. Kullanıcı tarafından belirlenen bir güvenlik seviyesine göre kaynak ve hedef düğümler arasında en uygun yol bulanık mantık ve genetik algoritmayla hesaplanmaktadır. Benzetim sonuçları, bulanık mantık ile dilsel olarak güvenlik seviyesi belirlenmiş yollar üzerinde genetik algoritmayla en kısa yol bulmanın başarılı olduğunu göstermiştir. Geliştirilen protokolün benzetimi ve en uygun parametrelerinin belirlenebilmesi için Matlab programı kullanılmıştır.In this thesis, a security-aware routing protocol is developed for wireless broadband mobile networks. The developed protocol is proactive and has nondeterministic routing algorithm. Genetic algorithm is used to find shortest path and fuzzy logic is used to define the level of security of paths as linguistic labels. Based on the user defined security level, the best path between the source and the destination is determined using fuzzy logic and genetic algorithm. Experimental results illustrated that obtaining the shortest path by genetic algorithm using the paths with linguistic security levels determined by fuzzy logic is successful. Matlab is used for network simulation and determination of suitable parameters of the developed protocol

PID kontrolörün karınca kolonisi genetik algoritma tabanlı optimizasyonu ve GUNT RT 532 basınç prosesinin kontrolü

PID KONTROLÖRÜN KARINCA KOLONİSİ / GENETİK ALGORİTMA TABANLI OPTİMİZASYONU ve GUNT RT 532 BASINÇ PROSESİNİN KONTROLÜ Bu tez, Marmara Üniversitesi Elektronik-Bilgisayar Eğitimi Bölümü Sayısal Kontrol Sistemleri Laboratuarında bulunan Gunt RT 532 Basınç Proses Kontrol sisteminin denetiminde kullanılan PID kontrolör katsayılarının optimal değerlerinin, genetik algoritma (GA) ve karınca kolonisi algoritması(KKA) ile bulunması ve sistemin bu katsayılar ile gerçek zamanlı kontrolü üzerinedir. Kontrol edilecek prosesin dinamik modeli yapay sinir ağı(YSA) kullanılarak elde edilmiştir. Dinamik modelinin oluşturulması sırasında, sisteme belli giriş değerleri verilmiş ve bu değerlere karşılık sistem çıkışları elde edilerek giriş-çıkış veri seti oluşturulmuştur. Bu veri seti kullanılarak proses için farklı katman ve nöron sayılı modeller oluşturulmuştur. Bu modeller farklı YSA performans fonksiyonlarına göre değerlendirilmiş ve hata değeri en küçük model, prosesin dinamik modeli olarak seçilmiştir. Seçilen model kullanılarak PID kontrolör KKA, GA ve Ziegler-Nichols (ZN) ile optimize edilmiştir. KKA ve GA teknikleri ile elde edilen sonuçlar, klasik teknik olan ZN ile elde edilen aşım, yükselme zamanı, oturma zamanı kriterlerine ve yörünge takibindeki karekök ortalama (Root Mean Square-RMS) hatasına göre karşılaştırılmıştır. GA ve KKA’ nın performanslarının ZN tekniğine göre daha iyi olduğu gözlenmiştir. OPTIMIZATION OF PID CONTROLLER USING ANT COLONY / GENETIC ALGORITHMS AND CONTROL OF THE GUNT RT 532 PRESSURE PROCESS This thesis describes, a real time control algorithm, using genetic algorithm (GA) and ant colony optimization (ACO) algorithm for optimizing PID controller parameters developed for Gunt RT 532 Pressure Process Control System in the Digital Control Systems Laboratory of Technical Education Faculty at Marmara University. The dynamic model of the process to be controlled was obtained using Artificial Neural Network (ANN). In development of the model, the system was run with different input and output values and, these were taken as the input-output data set. Using this data set, models with varying number of layers and neurons were constructed for the process. The model was evaluated on the basis of their performance functions. The model with minimum error was chosen as the dynamic model of the process. Using the chosen model, the parameters of PID controller were optimized with ACO, GA and Ziegler-Nichols (ZN) techniques. The performances of these three techniques were compared with each other using the criteria of overshoot, rise time, settling time and root mean square (RMS) error of the trajectory. It was observed that the performances of GA and ACO are better than that of ZN technique

A novel approach for classification of loads on plate structures using artificial neural networks

In this study the location of applied load on an aluminum and a composite plate was identified using two type of neural network classifiers. Surface Response to the Excitation (SuRE) method was used to excite and monitor the elastic guided waves on plates. The characteristic behavior of plates with and without load was obtained. The experiments were conducted using two set of equipment. First, laboratory equipment with a signal generator and a data acquisition card. Then same test was conducted with a low cost Digital Signal Processor (DSP) system. With experimental data, Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural network classifiers were used comparatively to detect the presence and location of load on both plates. The study indicated that the Neural Networks is reliable for data analysis and load diagnostic and using measurements from both laboratory equipment and low cost DSP. (C) 2016 Elsevier Ltd. All rights reserved

Implementation of heterodyning effect for monitoring the health of adhesively bonded and fastened composite joints

Composite materials are a preferred choice when high strength/weight ratio and resistance to corrosion are needed. For assembly, composite parts are joined by using adhesives and/or fasteners. Due to the increased use of composites, there is a need for reliable and affordable structural health monitoring (SHM) methods for the detection of weakened bonds and loosened fasteners. Heterodyne effect may be utilized for the evaluation of debonded area when the linear characteristics of the system changes to nonlinear as a result of light contact in the bonding zone and this nonlinear system responds to appropriate bitonal excitations with new frequencies. Nonlinear elastic wave spectroscopy (NEWS) methods are using the same concept although they are limited to the combination of a high and a low frequency. Heterodyne method allows the engineers to have control over the new output frequencies as indicators of nonlinearity in the target structure. In this study, implementation of the heterodyne method is proposed for identification of the debonded region and evaluation of the compressive forces applied to facing plates. The proposed SHM method proved to be effective in both scenarios. (C) 2018 Elsevier Ltd. All rights reserved

Development of comprehensive heterodyne effect based inspection (CHEBI) method for inclusive monitoring of cracks

Early detection of developing structural cracks is extremely important in improving safety and reducing maintenance costs in structural, aerospace, chemical, petrochemical, gas, and oil industries. Nonlinear structural health monitoring methods, such as wave modulation spectroscopy (NWMS), can detect the cracks in their early stages of development. Such methods usually rely on the combination of a high and a low-frequency, vibroacoustic, excitation for defect detection. However, a priori knowledge of the characteristics of the crack is required for selection of the appropriate frequency combination. In this study, the Comprehensive Heterodyne Effect Based Inspection (CHEBI) method is proposed to address this issue. The CHEBI method applies ascending and descending broadband frequency sweeps simultaneously to study the response of the structure in wide ranges of frequencies in the time-frequency domain. Therefore, it eliminates the need for multiple experimental tests to find appropriate combinations of the high and the low-frequency components. The proposed method detected cracks from 1mm to 25mm in length on a dog-bone shaped aluminum specimen. These results confirm that the CHEBI can be successfully applied for detection of cracks with varying severities without the need for adjustment of the excitation frequencies

Alüminyum 2024 T-3 plakadaki hasar boyutunun lamb dalgası ile tespiti

Bu çalışmada, hasar tespiti ve yapısal durum izleme için Alüminyum 2024 T-3 plakası üzerinde oluşturulan 2 ile 5 cm arası kesikler Lamb dalgaları ile incelenmiş ve Lamb dalgalarının simetrik ve asimetrik modlarının karşılaştırılması yapılmıştır. Toplanan yüzey frekans cevaplarının zarfları Hilbert dönüşümü ile elde edilmiş ve her bir hasarın zayıflama miktarı yüzdelik olarak gösterilmiştir. Elde edilen sonuçlar, her iki Lamb dalga modunun Alüminyum 2024 T-3 plakada oluşturulan kesik boyutlarının araştırılmasında kullanılabileceğini göstermektedir

Contact and non-contact approaches in load monitoring applications using surface response to excitation method

Surface response to excitation (SuRE) method is a low-cost alternative to electromechanical impedance based structural health monitoring (SHM) technique. The SuRE method uses one piezoelectric transducer to excite the surface of a structure with a sweep sine wave. Piezoelectric sensors or scanning laser vibrometer can be used to monitor the dynamic response of structure. In this study, the performance of the SuRE method was evaluated with the conventional piezoelectric elements and scanning laser vibrometer used as contact and non-contact sensors, respectively, for monitoring the presence of loads on the surface. In order to determine the accuracy and reliability of both monitoring approaches in detecting changes in level of applied load, three different experimental setups were studied. Response of a system in the presence of a single load applying and multiple loads applying and its performance in detecting tightness in a nut and bolt system were investigated. The spectrum of the dynamic response is collected at the optimal operating condition. Any significant change of the spectral characteristics may indicate defects, improper loading or loose fasteners. The performance of the SuRE method using contact and non-contact sensors indicated that both variations of the method could be successfully used in load monitoring applications. (C) 2016 Elsevier Ltd. All rights reserved

Determination of damage length in aluminum 2024 T-3 plate by lamb wave

In this study, cuts between 2 and 5 cm formed on Aluminum 2024 T-3 plate for damage detection and structural health monitoring were examined with Lamb waves and the symmetric and asymmetric modes of Lamb waves were compared. The envelopes of the acquired surface frequency responses are obtained by Hilbert transform and the amount of attenuation of each damage is shown as a percentage. The results show that both Lamb wave modes can be used to investigate the cut dimensions created in the Aluminum 2024 T-3 plate