Defect Detection And Classification Of Silicon Solar Wafer Featuring Nir Imaging And Improved Niblack Segmentation

Menghasilkan tenaga yang boleh diperbaharui berkuantiti tinggi memerlukan kecekapan yang tinggi dalam fabrikasi produk wafer silikon, yang juga merupakan komponen asas panel solar. Oleh yang demikian, pemeriksaan kualiti yang tinggi untuk wafer solar semasa proses pengeluaran sangat penting. Dalam tesis ini, sistem pengesanan kecacatan yang cekap dan automatik menggunakan strategi pengelasan dan kelompok termaju telah dicadangkan. Dalam kajian ini, satu skema mesin penglihatan untuk mengesan keretakan mikro dan kecacatan-kecacatan yang lain dalam pembuatan polihabluran dan mono kristal wafer solar dicadangkan dan dibangunkan. Pemeriksaan retak mikro sangat mencabar kerana kecacatan ini sangat kecil dan tidak boleh dilihat dengan mata kasar. Kewujudan struktur heterogenus yang lain dalam wafer solar seperti bahan-bahan kasar dan kawasan gelap menjadikan pemeriksaan lebih mencabar. Dalam tesis ini, sebuah inspektor retak mikro yang mengandungi pencahayaan inframerah yang dekat dan algoritma segmentasi Niblack yang diperbaharui telah dicadangkan. Keputusan emperikal dan visual menunjukkan ketepatan dan prestasi yang lebih baik dari segi angka merit Pratt dan kaedah penilaian yang lain berbanding dengan formula pengambangan Niblack yang sedia ada. Keputusan angka merit (FOM), ketepatan (ACC), pekali kesamaan dadu (DSC) dan sensitiviti yang masing-masingnya sentiasa lebih tinggi daripada 0.871, 99.35 %, 99.68 %, dan 99.75 % bagi imej-imej dalam kajian ini. Sementara itu, satu set deskriptor bersepadanan dengan penerangan ciri-ciri bentuk Fourier eliptik, diekstrak bagi setiap kecacatan yang telah dikesan, dan dinilai bagi setiap kluster bagi tujuan pengelompokan dan pengelasan. Pengelasan menggabungkan analisis ciri keamatan kecacatan, penggunaan tanpa pengawasan kelompok purata-k dan pelbagai kelas algoritma SVM. Kaedah-kaedah ini telah digunakan untuk pengesanan, pengelompokan dan klasifikasi imej wafer solar polihabluran, bersepadanan dengan kecacatan seperti keretakan mikro, kekotoran, dan cap jari. Keputusan kajian menunjukkan bahawa kaedah purata-k dan penklasifikasi SVM mampu mengelompok dengan tepat kecacatan-kecacatan tersebut dengan ketepatan, indeks Rand, dan Bayang indeks dengan nilai purata masing-masing sebanyak 99.8 %, 99.788 %, dan 98.43 %. ________________________________________________________________________________________________________________________ Producing a high yield of renewable energy requires a high efficiency in product fabrication of silicon wafers, which is the basic building component of solar panels. For this reason, the high quality inspection of solar wafers during the procedures of production is very important. In this thesis, an automatic and efficient defect detection system, utilising advanced classification and clustering strategies are proposed. In this study a machine vision scheme for detecting micro-cracks and other defects in polycrystalline and monocrystalline solar wafer manufacturing is proposed and developed. Micro-crack inspection is very challenging, because this type of defect is very small and completely invisible to the naked eye. The presence of other heterogeneous structures in solar wafers like grainy materials and dark regions further complicates the problem. In this study an efficient micro-crack inspector comprising near infrared illumination and an improved Niblack segmentation algorithm is proposed. Empirical and visual results demonstrate that the proposed solutions are competitive when compared to existing Niblack thresholding formulas and other standard methods, and achieve better precision and performance in terms of Pratt’s figure of merit and other evaluation methods. Result in a figure of merit (FOM), accuracy (ACC), dice similarity coefficient (DSC), and sensitivity were consistently higher than 0.871, 99.35 %, 99.68 %, and 99.75 %, respectively, for all images tested in this study. Meanwhile, a set of descriptors corresponding to Elliptic Fourier Features shape description is extracted for each defect and is evaluated for each cluster to use for clustering and classification part. The classification combines the analysis of defect intensity features, the application of unsupervised k-mean clustering and multi-class SVM algorithms. The methods have been applied for detecting, clustering and classification polycrystalline solar wafer images, corresponding to defects such as micro cracks, stain, and fingerprints. Results indicate that the k-mean and SVM classifier can accurately cluster the defects with accuracy, Rand index, and Silhouette index averaging at 99.8 %, 99.788 %, and 98.43 %, respectively

Theoretical and experimental studies on quantum correlations and memory effects in composite quantum systems

This doctoral dissertation presents groundbreaking research on the theoretical and experimental exploration of quantumcorrelations and memory effects in composite quantum systems. The work is divided into two main parts: Part I investi-gates the utilization of quantum correlations in identical quantum systems within a quantum information framework, whilePart II introduces a novel witness of non-Markovianity and evaluates its validity and efficiency across various systems.Part I highlights the indistinguishability of identical qubits as a fundamental quantum resource that can be harnessedwithin the spatially localized operations and classical communication (sLOCC) framework to conditionally generateentanglement. This probabilistic and controllable scheme comprises three stepsinitialization, deformation, and post-selectionenabling the generation of different classes of multipartite entangled states starting from a product state of Nspatially distinguishable identical qubits. Using graph-based representations, these schemes are mapped onto colored,complex, and weighted digraphs corresponding to specific experimental setups. Additionally, the analysis explores indis-tinguishability from an operational perspective, emphasizing its role in quantum metrology for quantum-enhanced phaseestimation using a NOON-like state (N=2) as a probe. It also demonstrates how quantum walks can achieve optimal phasesensing measurements. Lastly, the study examines experimentally controllable inhomogeneous quantum walk dynamicsas a platform for investigating the effects of coherent disorder on quantum correlations between indistinguishable photons,providing insights into dynamic quantum systems.Part II introduces a new witness of non-Markovianity and examines its validity and efficiency through various exam-ples. Inspired by the observation that non-Markovian effects can accelerate system dynamics and that quantum statisticalspeed quantifiers can determine the evolution time limit, a novel witness is proposed to characterize the non-Markovianbehavior of open quantum systems. This witness is based on the positive change rate of the Hilbert-Schmidt speed (HSS),a specific form of quantum statistical speed. A significant advantage of this witness is that it does not require the di-agonalization of the system’s evolved density matrix. Its efficiency is tested across low- and high-dimensional systemsas well as multiqubit open quantum systems. Furthermore, the study demonstrates the HSS-based witness as a reliabletool for evaluating and detecting global memory effects in both unital and non-unital correlated channels with varyingnoisy spectral densities. Additionally, it explores the impact of classical correlations between sequences of noisy quantumchannels on the non-Markovian memory effect.The contributions made in this thesis significantly advance the field of quantum information processing by enhancingour understanding of the role of indistinguishability in quantum phenomena and introducing a novel witness for non-Markovianity that provides practical tools for characterizing memory effects in open quantum systems. Together, thesefindings offer fundamental insights into quantum dynamics and open promising avenues for the development of futurequantum technologies

Failure prediction of steel components under the coupled effect of excessive plastic deformations and pitting corrosion

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Effect of the Joint Strength on the Performance of Ordinary Moment-resisting Frames Under a Progressive Collapse Situation

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Micromechanical Modeling of Corroded Steel Joints under Excessive Plastic Deformations

Under excessive plastic deformations, pitting corrosion can accelerate ductile fracture initiation in steel structures. For an accurate numerical prediction of ductile fracture in corrosion pits, a micromechanical fracture criterion along with a fine three-dimensional solid meshing is required. Previous studies on this topic are limited to simple plates; however, for a more detailed component, e.g., steel beam-to-column joint, implementing the pit geometry on the global model of the joint is challenging in terms of meshing and computational time. In this paper, two-level numerical modeling was employed to reduce the complexity of the problem. In this technique, submodels with refined mesh are used to perform micromechanical simulations and assess the ductility degradation of joints. For a case study joint, the pits near the edge of the web and flange plates were found to be the most critical and they can reduce the fracture initiation displacement of the joint by about 25%. On the other hand, the pits located on the edges of plates or far from the edges caused a negligible reduction in the fracture initiation displacement of the joint. These results suggest two-level numerical modeling as a viable technique to facilitate micromechanical simulation of pitting corrosion in corroded steel joints.acceptedVersio

Developing Fracture-Based Fragility Curves for Steel Components in Corrosive Environments

Author's Accepted ManuscriptThis material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://doi.org/10.1061/(ASCE)ST.1943-541X.00032.Under excessive plastic deformations, pitting corrosion accelerates ductile fracture initiation in steel components. Because of the stochastic and time-dependent nature of corrosion in steel material, the integrity of the steel components must be evaluated through a rational procedure in which corrosion uncertainties are considered to estimate the probability of failure for future events. Previous studies developed fragility curves to predict the capacity of global structures under uniform corrosion. However, for steel structures subjected to pitting corrosion, the local effect of corrosion is substantial and is also challenging to implement in the global model of structures. In this study, the concept of fracture-based fragility curves was developed at the component level by micromechanical modeling of different random pitting morphologies at a given intensity level of pitting corrosion. For this purpose, a unique meshing technique was employed to implement random pitting morphologies in numerical models. A demonstration study on a single-sided corroded plate revealed that random morphologies at an identical corrosion intensity level led to a notable dispersion in the failure elongations. The proposed fragility curves could address this effect on the probability of failure of the specimen. Therefore, decision-makers can reliably utilize such curves in a comprehensive risk-based corrosion management framework to evaluate the risk of failures and determine proper treatment strategies.acceptedVersio

The effects of foot reflexology on depression during menopause: A randomized controlled clinical trial

Objective: The purpose of this study was to determine the effects of foot reflexology on depression during menopause. Design: Randomized controlled clinical trial. Setting: Gynecology outpatient clinic. Interventions: We enrolled 90 menopausal women with depression. Participants were assigned to the intervention (n = 45) and control (n = 45) groups by block randomization. Participants in the intervention group received 15 min of foot re�exology on each foot for a total of 30 min in evenings, twice a week for six weeks. Participants in the control group received only the routine care for menopause patients. Main outcome measures: The Beck Depression questionnaire was completed by all participants at the beginning of the trial and the end of the intervention and two months after completion of the intervention. Results: A total of 121 patients were assessed for eligibility to participate in the study. One-hundred patients met the criteria to participate, and 90 participants�45 participants in each group�completed the study. In the intervention group, the mean scores of depression before, immediately after, and two months after the study were 26.97 ± 4.47 (95 CI = 25.3�28.3), 22.55 ± 5.18 (95 CI = 20.9�24.1), and 21.20 ± 5.74 (95 CI = 19.4�22.9), respectively. In the control group, these scores were 26.15 ± 5.01 (95 CI = 24.6�27.6), 26.22 ± 5.14 (95 CI = 24.7�27.7), and 26.66 ± 3.87 (95CI = 25.5�27.8), respectively. Using Repeated Measures ANOVA, the comparison of the mean scores of depression in the two groups indicated that the scores were decreased over time. Conclusion: The findings indicated that the foot reflexology technique can be effective for reducing women's depression during menopause. However, considering the study's limitations, including a small sample size and no intervention in the control group, more studies are needed to verify the findings. © 2019 Elsevier Lt

The Effect of Oxygen Therapy on Oxidative Stress Index in Patients with Acute Myocardial Infarction; a Letter to the Editor

Tissue hypoxia is a key factor for cell death after acute myocardial infarction (MI). It seems that increase in the relative oxygen pressure in inhaled air can be an effective treatment option for treating acute MI. However, contradicting findings and results have been published regarding using oxygen therapy in patients with acute MI (1, 2). Some researchers have believed that generation of free radicals, induction of oxidative stress, and damage to cell membrane are among side effects of O2 consumption (3, 4). It has been shown that O2 therapy can increase microvascular resistance, result in a decrease in coronary blood flow and cardiac output, and bring about numerous negative effects such as increase in the risk of arrhythmia and cellular damage (4)