• Journal of Astronomy and Space Sciences
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• v.38 no.3
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• pp.165-173
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• 2021

Apophis is a near-Earth object with a diameter of approximately 340 m, which will come closer to the Earth than a geostationary orbit in 2029, offering a unique opportunity for characterizing the object during the upcoming encounter. Therefore, Korea Astronomy and Space Science Institute has a plan to propose a space mission to explore the Apophis asteroid using scientific instruments such as a laser altimeter. In this study, we evaluate the performance metrics of a laser altimeter using a pseudorandom noise modulation technique for the Apophis mission, in terms of detection probability and ranging accuracy. The closed-form expression of detection probability is provided using the cross correlation between the received pulse trains and pseudo-random binary sequence. And the new ranging accuracy model using Gaussian error propagation is also derived by considering the sampling rate. The operation range is significantly limited by thermal noise rather than background noise, owing to not only the low power laser but also the avalanche photodiode in the analog mode operation. However, it is demonstrated from the numerical simulation that the laser altimeter can achieve the ranging performance required for a proximity operation mode, which employs commercially available components onboard CubeSat-scale satellites for optical communications.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.209-214
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• 2021

Panoptic segmentation, which is now widely used in computer vision such as medical image analysis, and autonomous driving, helps understanding an image with holistic view. It identifies each pixel by assigning a unique class ID, and an instance ID. Specifically, it can classify 'thing' from 'stuff', and provide pixel-wise results of semantic prediction and object detection. As a result, it can solve both semantic segmentation and instance segmentation tasks through a unified single model, producing two different contexts for two segmentation tasks. Semantic segmentation task focuses on how to obtain multi-scale features from large receptive field, without losing low-level features. On the other hand, instance segmentation task focuses on how to separate 'thing' from 'stuff' and how to produce the representation of detected objects. With the advances of both segmentation techniques, several panoptic segmentation models have been proposed. Many researchers try to solve discrepancy problems between results of two segmentation branches that can be caused on the boundary of the object. In this survey paper, we will introduce the concept of panoptic segmentation, categorize the existing method into two representative methods and explain how it is operated on two methods: top-down method and bottom-up method. Then, we will analyze the performance of various methods with experimental results.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.183-189
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• 2021

Transfer pricing is a matter of concern for countries. It affects the interests of the parties involved in the commercial transaction. Through manipulation of prices in transactions, businesses take advantage of tax rates in a country to adjust profits for economic gain. This affects the fairness and rationality of economic transactions between related parties. The article uses a two-year time series from 2018 to 2019 of 50 foreign direct investment enterprises in Vietnam. The article uses ordinary least squares to test the hypotheses of the research model. The article uses four independent variables related to ownership structure affecting transfer pricing decisions including total ownership, organization ownership, concentration ownership, and area ownership. Research results show that two variables have a positive influence on transfer pricing decisions including total ownership and organization ownership. Organization ownership has a higher degree of influence than total ownership. To be able to control transaction activities related to transfer pricing, Vietnam's state management agencies need to pay attention to perfecting the legal framework based on supplementing and amending regulations related to transfer pricing. Legal regulations need to be regulated based on international common practices to ensure uniformity on a global scale.

• Convergence Security Journal
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• v.21 no.1
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• pp.107-113
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• 2021

Recently, large-scale research and efforts aimed at the smart world such as smart city, smart home, smart transportation, and smart care are continuing. As these smart worlds become more common, the expansion of connectivity with the Internet and the threat of cyber terrorism will be inevitable. Increasing the threat of cyber terrorism is increasing the likelihood of a massive disaster and safety accident. Therefore, in this paper, we examine smart worlds that are expanded in various forms and derive the security threat factors that smart worlds have. In addition, it is proposed to block the threat of terrorism from abroad if access from abroad is not required when constructing a smart world. Through this, we intend to present a method to eliminate cyber terror threats for the establishment and operation of a safe smart world.

• Ocean and Polar Research
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• v.43 no.4
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• pp.295-306
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• 2021

A field scale productivity analysis is required for the development of gas hydrate in marine sedimentary layers to verify the field applicability of production techniques and to improve productivity. In this study, the productivity resulting from the application of depressurization using multi-wells for the development of gas hydrate in the Ulleung Basin, East Sea of Korea, was determined. A numerical analysis model reflecting the conditions of candidate sites for the Ulleung Basin was constructed, and the productivity and dissociation behavior were comparatively analyzed. The pressure propagation and gas hydrate dissociation region by the multi-wells were wider and the productivity was higher than that of a single well. Different depressurization effects according to the spacing of multi-wells affected productivity. The results provide basic data for productivity analysis when establishing a field test production plan for the Ulleung Basin.

• Journal of Korean Public Health Nursing
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• v.35 no.1
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• pp.120-134
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• 2021

Purpose: The reproductive health of women in early adulthood can affect pregnancy, childbirth, and menopause in later life. Menstrual symptoms not only affect daily life, but are also a reflection of a woman's reproductive health. This study was conducted to explore the factors affecting menstrual symptoms among university students. Methods: The general characteristics, life style, menstrual characteristics, stress, and menstrual symptom of 177 female students were assessed through an online survey at one university. An independent sample t-test, one-way variance analysis, correlation analysis, and multiple regression analysis were performed using the SPSS 22.0 program. Results: The menstrual symptom score was 91.68±32.11 points, and the score of 'mood change' was the highest. Stress (��=.38, p<.001), amount of menstruation (��=.20, p=.001), menarche age (��=-.18, p=.003), health problems (��=.16, p=.010), and age (��=.15, p=.016) were found to have a significant effect on menstrual symptom, and the explanatory power of the regression model was 40%. Conclusion: This study investigated the degree and influencing factors of menstrual symptom using the Korean Menstrual Symptom Scale (KMSS) developed for Korean university students. Among the factors influencing menstrual symptom, stress appeared to play a significant role. Stress management, observation of menstrual characteristics, and general health care are necessary to maintain a woman's reproductive health.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.127-134
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• 2019

The purpose of this study is to understand the purchasing intentions of Chinese consumers to Huawei and other domestic brands in the context of the current Sino-US trade war. Taking the mass consumers as the research object, this paper designs Likert five-level scale to investigate consumers' purchase intention of domestic products in the future, and uses SPSS 23.0 and AMOS 23.0 statistical software to analyze and process statistical data. Using questionnaire survey and exploratory factor analysis, this paper constructs a model to analyze the impact of consumer ethnocentrism on consumers' purchase intention. By summarizing the overall purchasing intention of consumers, it is concluded that the development of domestic brands in the context of trade war is facing difficulties and challenges in the future, but at the same time, we must seize the opportunity of consumers' ethnocentrism under this background to positively influence their purchasing intention, make up for shortcomings, eliminate overcapacity, and seek greater development through technological innovation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3714-3732
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• 2019

With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.

• Journal of Korean Critical Care Nursing
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• v.12 no.2
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• pp.13-25
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• 2019

Purpose : The purpose of this study was to identify factors influencing the professional self-concept of nurses working in intensive care units (ICUs). Methods : Data were collected from July 1 to August 15, 2014. The subjects were 206 ICU nurses working in four university hospitals in B and U cities, Korea. Their professional self-concept was measured using Arthur's Scale revised by Yoon (2012), and professional quality of life (QOL) was measured using Pro QOL Korean Ver. 5 developed by Stamm (2010). Data were analyzed with SPSS Ver. 18, using a t-test, ANOVA, Pearson's correlation coefficient, and multiple regression analysis. Results : Professional self-concept was significantly correlated with compassion satisfaction (r=.61, p<.001), and burn out (r=-.57, p<.001). The factors influencing professional self-concept were compassion satisfaction (${\beta}=.46$, p<.001), burn out (${\beta}=-.27$, p<.001), and education level (${\beta}=.14$, p =.014). The explanatory power of this model was 46.5%. Conclusion : The results suggest that the influencing factors found in this study should be considered when planning nursing intervention programs for improving the professional self-concept of ICU nurses.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.682-693
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• 2019

The traditional classification methods mostly assume that the data for class distribution is balanced, while imbalanced data is widely found in the real world. So it is important to solve the problem of classification with imbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructed with the reference space and measurement reference scale which is come from a single normal group, and thus it is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO is constructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead of orthogonal array and signal-to-noise ratio (SNR) to select the valid variables, in which G-means, F-measure, dimensionality reduction are regarded as the classification optimization target. This proposed method is also applied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTS and the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO method has better result of prediction accuracy and dimensionality reduction.