Cable Tension Monitoring using Non-Contact Vision-based Techniques

In cable-stayed bridges, the structural systems of tensioned cables play a critical role in structural and functional integrity. Thereby, tensile forces in the cables become one of the essential indicators in structural health monitoring (SHM). In this thesis, a video image processing technology integrated with cable dynamic analysis is proposed as a non-contact vision-based measurement technique, which provides a user-friendly, cost-effective, and computationally efficient solution to displacement extraction, frequency identification, and cable tension monitoring. In contrast to conventional contact sensors, the vision-based system is capable of taking remote measurements of cable dynamic response while having flexible sensing capability. Since cable detection is a substantial step in displacement extraction, a comprehensive study on the feasibility of the adopted feature detector is conducted under various testing scenarios. The performance of the feature detector is quantified by developing evaluation parameters. Enhancement methods for the feature detector in cable detection are investigated as well under complex testing environments. Threshold-dependent image matching approaches, which optimize the functionality of the feature-based video image processing technology, is proposed for noise-free and noisy background scenarios. The vision-based system is validated through experimental studies of free vibration tests on a single undamped cable in laboratory settings. The maximum percentage difference of the identified cable fundamental frequency is found to be 0.74% compared with accelerometer readings, while the maximum percentage difference of the estimated cable tensile force is 4.64% compared to direct measurement by a load cell

Variable-Frequency Ultrasonic Treatment on Microstructure and Mechanical Properties of ZK60 Alloy during Large Diameter Semi-Continuous Casting

Traditional fixed-frequency ultrasonic technology and a variable-frequency ultrasonic technology were applied to refine the as-cast microstructure and improve the mechanical properties of a ZK60 (Mg-Zn-Zr) alloy during large diameter semi-continuous casting. The acoustic field propagation was obtained by numerical simulation. The microstructure of the as-cast samples was characterized by optical and scanning electron microscopy. The variable-frequency ultrasonic technology shows its outstanding ability in grain refinement comparedwith traditional fixed-ultrasonic technology. The variable-frequency acoustic field promoted the formation of small beta-Mg globular grains and changed the distribution and morphology of beta-phases throughout the castings. Ultimate tensile strength and elongation are increased to 280 MPa and 8.9%, respectively, which are 19.1% and 45.9% higher than the values obtained from billets without ultrasonic treatment and are 11.6% and 18.7% higher than fixed-frequency ultrasound treated billets. Different refinement efficiencies appear in different districts of billets attributed to the sound attenuation in melt. The variable-frequency acoustic field improves the refinement effect by enhancing cavitation-enhanced heterogeneous nucleation and dendrite fragmentation effects

Thin-walled Cystic Lung Cancer: An Analysis of 24 Cases and Review of Literatures

Background and objective Lung cancer presenting as cystic lesions was first described by Anderson and Pierce in 1954. Lung cancer presenting as cysts is a rare entity in clinical practice. Differential diagnosis is difficult in the benign-like cyst. This study investigated the clinical characteristics, diagnosis and treatment of lung cancer presenting as cysts. Methods We conducted a retrospective analysis of the clinical records of 24 patients who underwent surgery for a primary lung cancer presenting as cysts in our department between 2007 and 2013. We defined a ‘Thin-walled cyst’ as a cavitary lesion with a wall thickness of 4 mm or less along at least 75% of the circumference of the lesion. The whole group underwent post-operative follow-up. Results The incidence of cystic lung cancer was 0.49% (24/4,897) of surgical cases. The subjects’ age ranged from 19 to 77 yr with a median age of 56.5 yr. Ten cases presented with respiratory symptoms while 14 showed abnormal shadows on a chest CT without symptoms. Histological analysis showed that 18 cases were of adenocarcinoma, three of squamous cell carcinoma, one of small cell carcinoma, one of adenosquamous carcinoma and one of large cell carcinoma. Three patients were dead, and the remaining 21 patients are alive and disease free at the end of follow-up. Conclusion Cystic lung cancer should be kept in mind during the differential diagnosis of focal benign cyst. Cystic lung cancer could achieve a good outcome if early diagnose can be obtained

miR-143 Inhibits NSCLC Cell Growth and Metastasis by Targeting Limk1

MicroRNAs (miRNAs) have essential roles in carcinogenesis and tumor progression. Here, we investigated the roles and mechanisms of miR-143 in non-small cell lung cancer (NSCLC). miR-143 was significantly decreased in NSCLC tissues and cell lines. Overexpression of miR-143 suppressed NSCLC cell proliferation, induced apoptosis, and inhibited migration and invasion in vitro. Integrated analysis identified LIM domain kinase 1 (Limk1) as a direct and functional target of miR-143. Overexpression of Limk1 attenuated the tumor suppressive effects of miR-143 in NSCLC cells. Moreover, miR-143 was inversely correlated with Limk1 expression in NSCLC tissues. Together, our results highlight the significance of miR-143 and Limk1 in the development and progression of NSCLC

Analyze Informant-Based Questionnaire for the Early Diagnosis of Senile Dementia Using Deep Learning

Objective: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire. Methods: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score. Results: Compared with the seven conventional machine learning algorithms, the DNN showed higher stability and achieved the best accuracy with 0.88, which also showed good results for identifying normal (F1-score=0.88), mild cognitive impairment (MCI) (F1-score=0.87), very mild dementia (VMD) (F1-score=0.77) and Severe dementia (F1-score=0.94). Conclusion: The deep neural network (DNN) classification model can effectively help doctors accurately screen patients who have normal cognitive function, mild cognitive impairment (MCI), very mild dementia (VMD), mild dementia (Mild), moderate dementia (Moderate), and severe dementia (Severe)