• Wind and Structures
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• v.1 no.1
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• pp.1-23
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• 1998

This paper describes wind investigations for the Leaning Tower of Pisa which were conducted as part of an overall evaluation of its behaviour. Normally a short, stiff and heavy building would not be a candidate for detailed wind analyses. However, because of extremely high soil pressures developed from its inclination, there has been increasing concern that environmental loading such as wind actions could combine with existing conditions to cause the collapse of the tower. The studies involved wind assessment at the site as a function of wind direction, analysis of historical wind data to determine extreme wind probabilities of occurrence, estimation of structural properties, analytical and boundary layer wind tunnel investigations of wind loads and evaluation of the response with special concern for loads in the direction of inclination of the tower and significant wake effects from the neighboring cathedral for critical wind directions. The conclusions discuss the role of wind on structural safety, the precision of results attained and possible future studies involving field measurements aimed at validating or improving the analytical and boundary layer wind tunnel based assessments.

• Smart Structures and Systems
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• v.7 no.4
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• pp.263-274
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• 2011

A reliability-based slope stability assessment method considering fluctuations in the monitored matric suction was proposed for real-time identification of slope risk. The assessment model was based on the limit equilibrium model for infinite slope failure. The first-order reliability method (FORM) was adopted to calculate the probability of slope failure, and results of the model were compared with Monte-Carlo Simulation (MCS) results to validate the accuracy and efficiency of the model. The analysis shows that a model based on Advanced First-Order Reliability Method (AFORM) generates results that are in relatively good agreement with those of the MCS, using a relatively small number of function calls. The contribution of random variables to the slope reliability index was also examined using sensitivity analysis. The results of sensitivity analysis indicate that the effective cohesion c' is a significant variable at low values of mean matric suction, whereas matric suction ($u_a-u_w$) is the most influential factor at high mean suction values. Finally, the reliability indices of an unsaturated model soil slope, which was monitored by a wireless matric suction measurement system, were illustrated as 2D images using the suggested probabilistic model.

• Journal of Korean Medicine for Obesity Research
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• v.2 no.1
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• pp.1-12
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• 2002

Eating disorders are psychiatric disorders characterized by abnormal eating patterns and cognitive distortions related to food, weight and shape, which is in turn result in adverse effects on nutrition status, medical complications, and impaired health status and function. The American Psychiatric Association's DSMIVTR offers two diagnoses to describe disordered eating anorexia nervosa and bulimia nervosa. A third category, eating disorder not otherwise specified(EONOS) include binge eating disorder. The prevalence of eating disorder has greatly increased among adolescence and young adults since 1990's when rapid import of western culture took place. It is likely that patients who ask for weight loss are at high risk of having eating disorder. Severe dietary restriction for weight loss may cause eating disorder. Therefore it is recomendable for doctors to have appropriate understanding and guidelines of eating disorder to help their patients.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.547-553
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• 2021

Dynamic object recognition is an important task for autonomous vehicles. Since dynamic objects exhibit a higher collision risk than static objects, our own trajectories should be planned to match the future state of moving elements in the scene. Time information such as optical flow can be used to recognize movement. Existing optical flow calculations are based only on camera sensors and are prone to misunderstanding in low light conditions. In this regard, to improve recognition performance in low-light environments, we applied a normalization filter and a correction function for Gamma Value to the input images. The low light quality improvement algorithm can be applied to confirm the more accurate detection of Object's Bounding Box for the vehicle. It was confirmed that there is an important in object recognition through image prepocessing and deep learning using YOLO.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.3100-3111
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• 2021

Numerical modeling for the safety-related equipment used in a nuclear power plant (i.e., cabinet facilities) plays an essential role in seismic risk assessment. A full finite element model is often time-consuming for nonlinear time history analysis due to its computational modeling complexity. Thus, this study aims to generate a simplified model that can capture the nonlinear behavior of the electrical cabinet. Accordingly, the distributed plasticity approach was utilized to examine the stiffness-degradation effect caused by the local buckling of the structure. The inherent dynamic characteristics of the numerical model were validated against the experimental test. The outcomes indicate that the proposed model can adequately represent the significant behavior of the structure, and it is preferred in practice to perform the nonlinear analysis of the cabinet. Further investigations were carried out to evaluate the seismic behavior of the cabinet under the influence of the constitutive law of material models. Three available models in OpenSees (i.e., linear, bilinear, and Giuffre-Menegotto-Pinto (GMP) model) were considered to provide an enhanced understating of the seismic responses of the cabinet. It was found that the material nonlinearity, which is the function of its smoothness, is the most effective parameter for the structural analysis of the cabinet. Also, it showed that implementing nonlinear models reduces the seismic response of the cabinet considerably in comparison with the linear model.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.254-266
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• 2021

The Internet of Medical Things (IoMTs) are to be considered an investment and an improvement to respond effectively and efficiently to patient needs, as it reduces healthcare costs, provides the timely attendance of medical responses, and increases the quality of medical treatment. However, IoMT devices face exposure from several security threats that defer in function and thus can pose a significant risk to how private and safe a patient's data is. This document works as a comprehensive review of modern approaches to achieving security within the Internet of Things. Most of the papers cited here are used been carefully selected based on how recently it has been published. The paper highlights some common attacks on IoMTs. Also, highlighting the process by which secure authentication mechanisms can be achieved on IoMTs, we present several means to detect different attacks in IoMTs

• Journal of Obesity & Metabolic Syndrome
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• v.27 no.4
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• pp.213-222
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• 2018

Bariatric surgery has evolved from a surgical measure for treating morbid obesity to an epochal remedy for treating metabolic syndrome as a whole, which is represented by type 2 diabetes mellitus. Numerous clinical trials have advocated bariatric or metabolic surgery over nonsurgical interventions because of markedly superior metabolic outcomes in morbidly obese patients who satisfy traditional criteria for bariatric surgery (body mass index [BMI] >$35kg/m^2$) and in less obese or simply overweight patients. Nevertheless, not all diabetes patients achieve the most desirable outcomes; i.e., diabetes remission after metabolic surgery. Thus, candidates for metabolic surgery should be carefully selected based on comprehensive preoperative assessments of the risk-benefit ratio. Predictors for diabetes remission after metabolic surgery may be classified into two groups based on mechanism of action. The first is indices for preserved pancreatic beta-cell function, including younger age, shorter duration of diabetes, and higher C-peptide level. The second is the potential for an insulin resistance reduction, including higher baseline BMI and visceral fat area. Several prediction models for diabetes remission have been suggested by merging these two to guide the joint decision-making process between clinicians and patients. Three such models, DiaRem, ABCD, and individualized metabolic surgery scores, provide an intuitive scoring system and have been validated in an independent external cohort and can be utilized in routine clinical practice. These prediction models need further validation in various ethnicities to ensure universal applicability.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.823-831
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• 2019

In recent years, there has been developed systems such as a surveillance system and access control using a face recognition function instead of a password or an RFID chip, thereby reducing the risk of falsification. Moreover, deep learning technology has been applied to real-time face recognition technology in video, so it makes possible the development of access control system that improves the accuracy of recognition and efficiency of management. In this paper, we propose a real-time access management system based on face recognition using ResNet. The system is based on web server, which make it possible to manage the access by recognizing the person of the image through the camera and access information stored in the database. It can be accessed by a user application to receive various information. The implemented system identifies a person in real time and allows access control by accurately distinguishing whether they are members or not, and the test results can recognize in 0.2 seconds. The accuracy of recognition rate is up to about 97% depending on the experiment environment. With this system, access can be managed quickly and effectively, even many people rush to it.

• Journal of the Korean society of biological therapies in psychiatry
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• v.24 no.3
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• pp.142-155
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• 2018

Insomnia in patients with hematopoietic stem cell transplantation(HSCT) has been underdiagnosed and undertreated. This study reviewed the frequency, characteristics, physical and psychological effects, and treatments of insomnia in HSCT patients to highlight clinical importance in this specialized population. Furthermore, the authors intended to suggest a model that would conceptualize insomnia in the context of HSCT. In the pre-transplant period, about half of patients with HSCT suffered from sleep disturbance. A substantial number of patients experienced distressing insomnia during the HSCT procedure and recovered to the level of the pre-transplant period. However, sleep disruption could be a chronic symptom in HSCT survivors and could negatively impact quality of control, cancer-related fatigue(CRF), immune function, and psychological distress. The 3P's model(Predisposing, Precipitating, Perpetuating) explains insomnia in cancer population and could be also relevant to HSCT patients with specific consideration of CRF, graft-versus-host diseases, specific properties of hematological disease, and protective isolated milieu. Effective treatment of insomnia in HSCT includes non-pharmacological(e.g., cognitive behavioral therapy, environmental modification) and pharmacological interventions. The decision of pharmacological treatment should be based on the issue of safety due to high risk of potential drug-drug interactions. Screening, treatment, and further research of insomnia in HSCT patients using validated subjective and/or objective measures are warranted.

• Journal of Veterinary Clinics
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• v.36 no.1
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• pp.42-45
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• 2019

Thromboelastography (TEG) analysis consists of ${\alpha}$, G, K, MA, and R types of values and tests the effectiveness of blood clotting, which can be assessed for platelet function, clotting strength, and fibrinolysis. Canine heartworm diseases caused by Dirofilaria immitis, a vascular nematode, can lead to hyperfibrinolysis and hypercoagulation. In this study, G and MA values showed a statistically significant decrease over time after treatment of heartworm disease. Additionally, the ${\alpha}-value$ showed a high correlation with G, K, MA, and R values. The G value showed a high correlation with K and MA values, while The K value showed a high correlation with MA and R values. This study clearly found a gradual decrease in G and MA values in dogs with heartworm disease over time, both before and after treatment. This suggests that the clot formation time is longer and that the intensity of clot formation is lowered and may improve the risk of thromboembolism in dogs with heartworm disease.