• Structural Monitoring and Maintenance
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• v.5 no.4
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• pp.463-488
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• 2018

In the last decades, research efforts have been spent to investigate the effect of prestressing on the dynamic behaviour of prestressed concrete (PSC) beams. Whereas no agreement has been reached among the achievements obtained by different Researchers and among the theoretical and the experimental results for simply supported beams, very few researches have addressed this problem in continuous PSC beams. This topic is, indeed, worthy of consideration bearing in mind that many relevant bridges and viaducts in the road and railway networks have been designed and constructed with this structural scheme. In this paper the attention is, thus, focused on the dynamic features of continuous PSC bridges taking into account the effect of prestressing. This latter, in fact, contributes to the modification of the distribution of the bending stress along the beam, also by means of the secondary moments, and influences the flexural stiffness of the beam itself. The dynamic properties of a continuous, two spans bridge connected by a nonlinear spring have been extracted by solving an eigenvalue problem in different linearized configurations corresponding to different values of the prestress force. The stiffness of the nonlinear spring has been calculated considering the mechanical behaviour of the PSC beam in the uncracked and in the cracked stage. The application of the proposed methodology to several case studies indicates that the shift from the uncracked to the cracked stage due to an excessive prestress loss is clearly detectable looking at the variation of the dynamic properties of the beam. In service conditions, this shift happens for low values of the prestress losses (up to 20%) for structure with a high value of the ratio between the permanent load and the total load, as happens for instance in long span, continuous box bridges. In such conditions, the detection of the dynamic properties can provide meaningful information regarding the structural state of the PSC beam.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.100-105
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• 2019

As IoT Technology develops and Era of Hyperconnectivity comes, various kinds of customized services became available. As a next-generation display, a smart mirror accesses multimedia devices and provides various services, so it can serve as a social learning tool for the children and the old ones, as well as adults who need information. Smart Mirror must be able to identify users for individualized services. However, since the Smart Mirror is an easily accessible device, there is a possibility that information such as an individual's pattern and habit stored in the smart mirror may be exposed to the outside. Also, the other possibility of leakage of personal location information is through personal schedule or appointment stored in the smart mirror, and another possibility that privacy can be violated is through checking the health state via personal photographs. In this research, we propose a system that identify users by the information the users registered about their physique just like their face, one that provides individually customized service to users after identifying them, and one which provides minimal information and service for unauthenticated users.

• Journal of Korean Society on Water Environment
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• v.37 no.3
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• pp.204-216
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• 2021

It is important to understand the factors influencing the temporal and spatial variability of water quality in order to establish an effective customized management strategy for contaminated aquatic ecosystems. In this study, the spatial diversity of the 5-year (2015 - 2019) average total phosphorus (TP) concentration observed in 40 Total Maximum Daily Loads unit-basins in the Nakdong River watershed was analyzed using 50 predictive variables of watershed characteristics, climate characteristics, land use characteristics, and soil characteristics. Cross-correlation analysis, a two-stage exhaustive search approach, and Bayesian inference were applied to identify predictors that best matched the time-averaged TP. The predictors that were finally identified included watershed altitude, precipitation in fall, precipitation in winter, residential area, public facilities area, paddy field, soil available phosphate, soil magnesium, soil available silicic acid, and soil potassium. Among them, it was found that the most influential factors for the spatial difference of TP were watershed altitude in watershed characteristics, public facilities area in land use characteristics, and soil available silicic acid in soil characteristics. This means that artificial factors have a great influence on the spatial variability of TP. It is expected that the proposed statistical modeling approach can be applied to the identification of major factors affecting the spatial variability of the temporal average state of various water quality parameters.

• Journal of Animal Reproduction and Biotechnology
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• v.36 no.1
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• pp.35-44
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• 2021

A pregnancy diagnosis is an important standard for control of livestock's reproduction in paricular dairy cattle. High reproductive performance in dairy animals is a essential condition to realize of high life-time production. Pregnancy diagnosis is crucial to shortening the calving interval by enabling the farmer to identify open animals so as to treat or re-breed them at the earliest opportunity. MicroRNAs are short RNA molecules which are critically involved in regulating gene expression during both health and disease. This study is sought to establish the feasible of circulating miRNAs as biomarkers of early pregnancy in cattle. We applied Illumina small-RNA sequencing to profile miRNAs in plasma samples collected from 12 non-pregnant cows ("open" cows: samples were collected before insemination (non-pregnant state) and after pregnancy check at the indicated time points) on weeks 0, 4, 8, 12 and 16. Using small RNA sequencing we identified a total of 115 miRNAs that were differentially expressed weeks 16 relative to non-pregnancy ("open" cows). Weeks 8, 12 and 16 of pregnancy commonly showed a distinct increase in circulating levels of miR-221 and miR-320a. Through genome-wide analyses we have successfully profiled plasma miRNA populations associated with pregnancy in cattle. Their application in the field of reproductive biology has opened up opportunities for research communities to look for pregnancy biomarker molecules in dairy cattle.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.2
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• pp.47-55
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• 2021

In order to ensure the fact that eight principle pattern differentiation is used clinically as a basic guideline for Korean medicine practice, the definition, role and method of yin-yang pattern differentiation with its case report were explored at first. Yinyang Pattern Differentiation is a method of discriminating human tendencies or morbidity based on the yin and yang characteristics expressed in living bodies. And yin and yang are the two contrasting characteristics and aspects of the interaction when certain physical conditions that have a lasting effect on the human physiological metabolic function are correlated with the morbidity. Specific methods of yinyang pattern differentiation can be divided into several types of yin and yang indicators. First, time and space factors like day and night, hot and cold seasons, above and below, topographical districts. Second, colors and pulse and their/or relative clearness and muddiness, hardness and softness, moving and resting. Third, diagnose yin and yang patterns through distinguishing the true and false of a fever and cold in an emergency phase such as increase of brain pressure and shock state. Fourth, general characteristics of the propensity and constitution of a subject such as body type, speech, behavior, and physiological metabolism. And for clinical use, these were summarized again as a symptom indicators of physical signs and color, pulse, tongue and questionnaire indicators of propensity, body type, and space-time characteristics. Conclusively, it was confirmed that yinyang pattern differentiation has its own diagnostic significance which is distinct from exterior-interior, cold-heat and deficiency-excess pattern differentiation.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.443-448
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• 2022

This paper presents a subband affine projection algorithm with variable step size to improve convergence performance in adaptive filtering applications with long adaptive filters and highly correlated input signals. The proposed algorithm can obtain fast convergence speed and small steady-state error by using different step sizes for each adaptive sub-filter in the subband structure to which polyphase decomposition and noble identity are applied. The step size derived to minimize the mean square error of the adaptive filter at each update time shows better convergence performance than the existing algorithm using a variable step size. In order to confirm the convergence performance of the proposed algorithm, which is superior to the existing algorithm, computer simulations are performed for mean square deviation(MSD) for AR(1) and AR(2) colored input signals considering the system identification model.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.