• Computers and Concrete
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• v.23 no.2
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• pp.81-95
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• 2019

The unexpected seismic interaction of dry-assembled precast concrete frame structures typical of the European heritage with their precast cladding panels brought to extensive failures of the panels during recent earthquakes due to the inadequateness of their connection systems. Following this recognition, an experimental campaign of cyclic and pseudo-dynamic tests has been performed at ELSA laboratory of the Joint Research Centre of the European Commission on a full-scale prototype of precast structure with vertical and horizontal cladding panels within the framework of the Safecladding project. The panels were connected to the frame structure by means of innovative arrangements of fastening systems including isostatic, integrated and dissipative. Many of the investigated configurations involved a strong frame-cladding interaction, modifying the structural behaviour of the frame turning it into highly non-linear since small deformation. In such cases, properly modelling the connections becomes fundamental in the framework of a design by non-linear dynamic analysis. This paper presents the peculiarities of the numerical models of precast frame structures equipped with the various cladding connection systems which have been set to predict and simulate the experimental results from pseudo-dynamic tests. The comparison allows to validate the structural models and to derive recommendations for a proper modelling of the different types of existing and innovative cladding connection systems.

• Journal of Korean Academy of Nursing
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• v.42 no.2
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• pp.302-309
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• 2012

Purpose: This study aimed to identify contents and trends of Korean nursing doctoral dissertations in terms of research methodology and theoretical characteristics. Methods: The design of the study was descriptive study and a total of 1,089 quantitative studies completed between 1982 and 2010 were reviewed using the analytical framework developed by the researchers. Results: The majority of studies utilized the experimental design (51.5%) and the others were survey design (38.8%) and methodological design (5.0%). Study subjects were shown as patients (45%), care givers (11.2%), ordinary persons (40.6%) and others (3.2%). There were growing trends in experimental design and patients as subjects. The prevailing data collection settings were hospitals (45.8%) and community (27.8%). The theoretical frameworks that studies were based on were the existing theories (37%) and a newly developed theoretical framework by a researcher (25.2%). a framework derived from other studies by the researcher (25.2%). Majority of studies (78.5%) employed a single theory as a theoretical framework. However, 31.8% of studies had no theoretical framework based on. Conclusion: Findings of this study provided the opportunities to shed new light on the current status of Korean doctoral dissertation and to deliberate on the future direction of nursing studies in Korea.

• Structural Monitoring and Maintenance
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• v.7 no.2
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• pp.149-166
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• 2020

This paper presents the results of an experimental investigation on a vibration-based damage identification framework for a steel girder type and a truss bridge based on acceleration responses to operational loading. The method relies on sensor clustering-based time-series analysis of the operational acceleration response of the bridge to the passage of a moving vehicle. The results are presented in terms of Damage Features from each sensor, which are obtained by comparing the actual acceleration response from the sensors to the predicted response from the time-series model. The damage in the bridge is detected by observing the change in damage features of the bridge as structural changes occur in the bridge. The relative severity of the damage can also be quantitatively assessed by observing the magnitude of the changes in the damage features. The experimental results show the potential usefulness of the proposed method for future applications on condition assessment of real-life bridge infrastructures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3865-3883
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• 2016

Infrastructure as a Service (IaaS) encapsulates computer hardware into a large amount of virtual and manageable instances mainly in the form of virtual machine (VM), and provides rental service for users. Currently, VM anomaly incidents occasionally occur, which leads to performance issues and even downtime. This paper aims at detecting anomalous VMs based on performance metrics data of VMs. Due to the dynamic nature and increasing scale of IaaS, detecting anomalous VMs from voluminous correlated and non-Gaussian monitored performance data is a challenging task. This paper designs an anomaly detection framework to solve this challenge. First, it collects 53 performance metrics to reflect the running state of each VM. The collected performance metrics are testified not to follow the Gaussian distribution. Then, it employs independent components analysis (ICA) instead of principal component analysis (PCA) to extract independent components from collected non-Gaussian performance metric data. For anomaly detection, it employs multi-class Bayesian classification to determine the current state of each VM. To evaluate the performance of the designed detection framework, four types of anomalies are separately or jointly injected into randomly selected VMs in a campus-wide testbed. The experimental results show that ICA-based detection mechanism outperforms PCA-based and LDA-based detection mechanisms in terms of sensitivity and specificity.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.134-143
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• 2010

This paper proposes a framework for classifying metric-based software quality prediction models, especially case of software criticality, into four types. Models are classified along two vectors: input metric forms and the necessity of past project data. Each type has its own characteristics and its strength and weakness are compared with those of other types using newly defined criteria. Through this qualitative evaluation each organization can choose a proper model to suit its environment. My earlier studies of criticality prediction model implemented specific models in each type and evaluated their prediction performances. In this paper I analyze the experimental results and show that the characteristics of a model type is the another key of successful model selection.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.5
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• pp.271-280
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• 2018

This paper presents a framework for developing aftershock fragility curves for reinforced concrete bridges initially damaged by mainshocks. The presented aftershock fragility is a damage-dependent fragility function, which is conditioned on an initial damage state resulting from mainshocks. The presented framework can capture the cumulative damage of as-built bridges due to mainshock-aftershock sequences as well as the reduced vulnerability of bridges repaired with CFRP pier jackets. To achieve this goal, the numerical model of column jackets is firstly presented and then validated using existing experimental data available in literature. A four-span concrete box-girder bridge is selected as a case study to examine the application of the presented framework. The aftershock fragility curves are derived using response data from back-to-back nonlinear dynamic analyses under mainshock-aftershock sequences. The aftershock fragility curves for as-built bridge columns are firstly compared with different levels of initial damage state, and then the post-repair effect of FRP pier jacket is examined through the comparison of aftershock fragility curves for as-built and repaired piers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4021-4037
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• 2018

In network intrusion detection research, two characteristics are generally considered vital to building efficient intrusion detection systems (IDSs): an optimal feature selection technique and robust classification schemes. However, the emergence of sophisticated network attacks and the advent of big data concepts in intrusion detection domains require two more significant aspects to be addressed: employing an appropriate big data computing framework and utilizing a contemporary dataset to deal with ongoing advancements. As such, we present a comprehensive approach to building an efficient IDS with the aim of strengthening academic anomaly detection research in real-world operational environments. The proposed system has the following four characteristics: (i) it performs optimal feature selection using information gain and branch-and-bound algorithms; (ii) it employs machine learning techniques for classification, namely, Logistic Regression, Naïve Bayes, and Random Forest; (iii) it introduces bulk synchronous parallel processing to handle the computational requirements of large-scale networks; and (iv) it utilizes a real-time contemporary dataset generated by the Information Security Centre of Excellence at the University of Brunswick (ISCX-UNB) to validate its efficacy. Experimental analysis shows the effectiveness of the proposed framework, which is able to achieve high accuracy, low computational cost, and reduced false alarms.

• ETRI Journal
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• v.37 no.6
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• pp.1199-1210
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• 2015

Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer-based automated two-phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU-to-emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two-phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components - multiple AU detection, AU detection fusion, and AU-to-emotion mapping. The experimental results on two real-world face databases demonstrate an improved performance over the previous two-phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.808-826
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• 2017

Most of conventional DSS's(Digital Signage Systems) have been built based on LAMP framework. Recent researches have shown that MEAN or MERN stack framework is simpler, more flexible, faster and more suitable for web-based application than LAMP stack framework. In this paper, we propose a design and implementation of MEARN (ME(A+R)N) stack-based real-time digital signage system, MR-DSS, which supports handing real-time tasks like urgent/instant messaging, system status monitoring and so on, efficiently in addition to conventional digital signage CMS service tasks. MR-DSCMS, CMS of MR-DSS, is designed to provide most of its normal services by REST APIs and real-time services like urgent/instant messaging by Socket.IO base under MEARN stack environment. In addition to architecture description of components composing MR-DSS, design and implementation issues are clarified in more detail. Through experimental testing, it is shown that 1) MR-DSS works functionally well, 2) the networking load performance of MR-DSCMS's REST APIs is better compared to a well-known open source Xibo CMS, and 3) real-time messaging via Socket.IO works much faster than REST APIs.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.1-9
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• 2019

Single image rain removal is a typical inverse problem which decomposes the image into a background scene and a rain streak. Recent works have witnessed a substantial progress on the task due to the development of convolutional neural network (CNN). However, existing CNN-based approaches train the network with synthetically generated training examples. These data tend to make the network bias to the synthetic scenes. In this paper, we present an unsupervised framework for removing rain streaks from real-world rainy images. We focus on the natural phenomena that static rainy scenes capture a common background but different rain streak. From this observation, we train siamese network with the real rain image pairs, which outputs identical backgrounds from the pairs. To train our network, a real rainy dataset is constructed via web-crawling. We show that our unsupervised framework outperforms the recent CNN-based approaches, which are trained by supervised manner. Experimental results demonstrate that the effectiveness of our framework on both synthetic and real-world datasets, showing improved performance over previous approaches.