• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.6
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• pp.315-329
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• 2018

Recently, RC transfer slab systems have been used widely to construct high-rise wall-type apartments for securing parking space or public space. However, it is problem that the design method and structural performance evaluation method developed for thin RC flat slab are still used in the design of the transfer slab whose thickness is very thick and therefore structural behavior is expected to be different from RC flat slab. Thus, for the rational design of the transfer slab, the ultimate shear behavior of the RC transfer slab system is required to be analyzed properly. Accordingly, in the present study, the two-way shear behavior of the transfer slab was analyzed using nonlinear FEM according to various design parameters such as thickness of the transfer slab, strength of concrete, shear span ratio, and reinforcement ratio. In addition, the two-way shear strength evaluations of RC transfer slab by the existing evaluation methods were verified by comparing those with the results of nonlinear FEM analysis.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1303-1314
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• 2019

This paper studies the harmonic characteristics of ship electric propulsion systems and their treatment methods. It also adopts effective measures to suppress and prevent ship power systems from affecting ship operation due to the serious damage caused by harmonics. Firstly, the harmonic characteristics of a ship electric propulsion system are reviewed and discussed. Secondly, aiming at problems such as resonant frequency and filter characteristics variations, resonance point migration, and unstable filtering performances in conventional passive filters, a method for fully tuning of a passive dynamic tunable filter (PDTF) is proposed to realize harmonic suppression. Thirdly, to address the problems of the uncontrollable inductance L of traditional air gap iron core reactors and the harmonics of power electronic impedance converters (PEICs), this paper proposes an electromagnetic coupling reactor with impedance transformation and harmonic suppression characteristics (ECRITHS), with the internal filter (IF) designed to suppress the harmonics generated by PEICs. The ECRITHS is characterized by both harmonic suppression and impedance change. Fourthly, the ECRITHS is investigated. This investigation includes the harmonic suppression characteristics and impedance transformation characteristics of the ECRITHS at the fundamental frequency, which shows the good performance of the ECRITHS. Simulation and experimental evaluations of the PDTF are carried out. Multiple PDTFs can be configured to realize multi-order simultaneous dynamic filtering, and can effectively eliminate the current harmonics of ship electric propulsion systems. This is done to reduce the total harmonic distortion (THD) of the supply currents to well below the 5% limit imposed by the IEEE-519 standard. The PDTF also can eliminate harmonic currents in different geographic places by using a low voltage distribution system. Finally, a detailed discussion is presented, with challenges and future implications discussed. The research results are intended to effectively eliminate the harmonics of ship electric power propulsion systems and to improve the power quality of ship power systems. This is of theoretical and practical significance for improving the power quality and power savings of ship power systems.

• Ocean Systems Engineering
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• v.9 no.3
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• pp.287-328
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• 2019

Comprehensive understanding of the flood risk assessments via frequency analysis often demands multivariate designs under the different notations of return periods. Flood is a tri-variate random consequence, which often pointing the unreliability of univariate return period and demands for the joint dependency construction by accounting its multiple intercorrelated flood vectors i.e., flood peak, volume & durations. Selecting the most parsimonious probability functions for demonstrating univariate flood marginals distributions is often a mandatory pre-processing desire before the establishment of joint dependency. Especially under copulas methodology, which often allows the practitioner to model univariate marginals separately from their joint constructions. Parametric density approximations often hypothesized that the random samples must follow some specific or predefine probability density functions, which usually defines different estimates especially in the tail of distributions. Concentrations of the upper tail often seem interesting during flood modelling also, no evidence exhibited in favours of any fixed distributions, which often characterized through the trial and error procedure based on goodness-of-fit measures. On another side, model performance evaluations and selections of best-fitted distributions often demand precise investigations via comparing the relative sample reproducing capabilities otherwise, inconsistencies might reveal uncertainty. Also, the strength & weakness of different fitness statistics usually vary and having different extent during demonstrating gaps and dispensary among fitted distributions. In this literature, selections efforts of marginal distributions of flood variables are incorporated by employing an interactive set of parametric functions for event-based (or Block annual maxima) samples over the 50-years continuously-distributed streamflow characteristics for the Kelantan River basin at Gulliemard Bridge, Malaysia. Model fitness criteria are examined based on the degree of agreements between cumulative empirical and theoretical probabilities. Both the analytical as well as graphically visual inspections are undertaken to strengthen much decisive evidence in favour of best-fitted probability density.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.85-95
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• 2018

With the popularity of smartphones, most peoples have been using mobile cameras to capture photographs. However, due to insufficient amount of lights in a low lighting condition, unwanted noises can be generated during image acquisition. To remove the noise, a method of using deep convolutional neural networks is introduced. However, this method still lacks the ability to describe textures and edges, even though it has made significant progress in terms of visual quality performance. Therefore, in this paper, the HOG (Histogram of Oriented Gradients) images that contain information about edge orientations are used. More specifically, a method of learning deep convolutional neural networks is proposed by stacking noise and HOG images into an input tensor. Experiment results confirm that the proposed method not only can obtain excellent result in visual quality evaluations, compared to conventional methods, but also enable textures and edges to be improved visually.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.1-17
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• 2021

Pseudo relevance feedback (PRF) is a powerful query expansion (QE) technique that prepares queries using the top k pseudorelevant documents and choosing expansion elements. Traditional PRF frameworks have robustly handled vocabulary mismatch corresponding to user queries and pertinent documents; nevertheless, expansion elements are chosen, disregarding similarity to the original query's elements. Word embedding (WE) schemes comprise techniques of significant interest concerning QE, that falls within the information retrieval domain. Deep averaging networks (DANs) defines a framework relying on average word presence passed through multiple linear layers. The complete query is understandably represented using the average vector comprising the query terms. The vector may be employed for determining expansion elements pertinent to the entire query. In this study, we suggest a DANs-based technique that augments PRF frameworks by integrating WE similarities to facilitate Arabic information retrieval. The technique is based on the fundamental that the top pseudo-relevant document set is assessed to determine candidate element distribution and select expansion terms appropriately, considering their similarity to the average vector representing the initial query elements. The Word2Vec model is selected for executing the experiments on a standard Arabic TREC 2001/2002 set. The majority of the evaluations indicate that the PRF implementation in the present study offers a significant performance improvement compared to that of the baseline PRF frameworks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.6
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• pp.1293-1300
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• 2012

With the rapid evolution of mobile devices and the development of wireless networks, users of mobile social network service on smartphone have been increasing. Also the security of personal information as a result of real-time communication and information-sharing are becoming a serious social issue. In this paper, a framework that can be linked with a social network services platform is designed using OpenAPI. In addition, we propose an authentication and detection mechanism to enhance the level of personal information security. The authentication scheme is based on an user ID and password, while the detection scheme analyzes user-designated input patterns to verify in advance whether personal information protection guidelines are met, enhancing the level of personal information security in a social network service environment. The effectiveness and validity of this study were confirmed through performance evaluations at the end.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.270-281
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• 2021

With the development of information technology, the size of insurance fraud is increasing rapidly every year, and the method is being organized and advanced in conspiracy. Although various forms of prediction models are being studied to predict and detect this, insurance-related information is highly sensitive, which poses a high risk of sharing and access and has many legal or technical constraints. In this paper, we propose a machine learning insurance fraud prediction model based on blockchain, one of the most popular technologies with the recent advent of the Fourth Industrial Revolution. We utilize blockchain technology to realize a safe and trusted insurance information sharing system, apply the theory of social relationship analysis for more efficient and accurate fraud prediction, and propose machine learning fraud prediction patterns in four stages. Claims with high probability of fraud have the effect of being detected at a higher prediction rate at an earlier stage, and claims with low probability are applied differentially for post-reference management. The core mechanism of the proposed model has been verified by constructing an Ethereum local network, requiring more sophisticated performance evaluations in the future.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.400-409
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• 2021

Training, unlike education in holistic perspective, not only affects individual's potential of work competencies such as knowledge and technology, but also affects the job performance in organizational perspective. The effectiveness of training for civil servants who participated in mock jury trial learning of Suwon-si was verified using CIPP model. Participants valued the significance of the process of training rather than the conclusion that draws a certain solution, and based on the comparison and analysis of pre·post evaluations, the educational effect of mock trial training and its utilization at work in the future was higher than before the training, proving its effectiveness. In terms of the implications of training programs in addition to the program planner's evaluation, participant constitution in consideration of sex or employment period is needed for learning interaction among various learners, and information acquisition training on various local issues and conflict situations, as well as the combination of deliberation learning, are needed. in addition, it may be supplemented through customized conflict management raining related to pending projects for the effectiveness of training.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.68-79
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• 2021

On the Social Internet of Things (SIoT), social activities occur through which the vehicle generates a variety of data, shares them with other vehicles, and sends and receives feedbacks. In order to share reliable information between vehicles, it is important to determine the reliability of a vehicle. In this paper, we propose a vehicle trust evaluation scheme to share reliable information among vehicles. The proposed scheme calculates vehicle trust by considering user reputation and network trust based on inter-vehicle social behaviors. The vehicle may choose to scoring, ignoring, redistributing, etc. in the social activities inter vehicles. Thereby, calculating the user's reputation. To calculate network trust, distance from other vehicles and packet transmission rate are used. Using user reputation and network trust, local trust is calculated. It also prevents redundant distribution of data delivered during social activities. Data from the Road Side Unit (RSU) can be used to overcome local data limitations and global data can be used to calculate a vehicle trust more accurately. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.